DiabSense: early diagnosis of non-insulin-dependent diabetes mellitus using smartphone-based human activity recognition and diabetic retinopathy analysis with Graph Neural Network

Non-Insulin-Dependent Diabetes Mellitus (NIDDM) is a chronic health condition caused by high blood sugar levels, and if not treated early, it can lead to serious complications i.e. blindness. Human Activity Recognition (HAR) offers potential for early NIDDM diagnosis, emerging as a key application f...

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Vydáno v:Journal of big data Ročník 11; číslo 1; s. 103 - 37
Hlavní autoři: Alam, Md Nuho Ul, Hasnine, Ibrahim, Bahadur, Erfanul Hoque, Masum, Abdul Kadar Muhammad, Urbano, Mercedes Briones, Vergara, Manuel Masias, Uddin, Jia, Ashraf, Imran, Samad, Md. Abdus
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cham Springer International Publishing 01.12.2024
Springer Nature B.V
SpringerOpen
Témata:
Acknowledgment
Activities of daily living
Artificial neural networks
Big Data
Big Data and Artificial Intelligence in Emerging Scientific Fields
Blindness
Chronic illnesses
Communications Engineering
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Database Management
Diabetes
Diabetes mellitus
Diabetic retinopathy
Diagnosis
Diagnostic tests
Graph Neural Network
Graph neural networks
Human activity recognition
Humans
Information Storage and Retrieval
Insulin
Mathematical Applications in Computer Science
Medical diagnosis
Networks
Neural networks
NIDDM
Patients
Recognition
Research subjects
Retinal images
Retinopathy
Risk factors
Smartphones
Symptoms
Diabetic retinopathy
Diabetes
Human activity recognition
Graph Neural Network
NIDDM
ISSN:2196-1115, 2196-1115
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Abstract Non-Insulin-Dependent Diabetes Mellitus (NIDDM) is a chronic health condition caused by high blood sugar levels, and if not treated early, it can lead to serious complications i.e. blindness. Human Activity Recognition (HAR) offers potential for early NIDDM diagnosis, emerging as a key application for HAR technology. This research introduces DiabSense, a state-of-the-art smartphone-dependent system for early staging of NIDDM. DiabSense incorporates HAR and Diabetic Retinopathy (DR) upon leveraging the power of two different Graph Neural Networks (GNN). HAR uses a comprehensive array of 23 human activities resembling Diabetes symptoms, and DR is a prevalent complication of NIDDM. Graph Attention Network (GAT) in HAR achieved 98.32% accuracy on sensor data, while Graph Convolutional Network (GCN) in the Aptos 2019 dataset scored 84.48%, surpassing other state-of-the-art models. The trained GCN analyzed retinal images of four experimental human subjects for DR report generation, and GAT generated their average duration of daily activities over 30 days. The daily activities in non-diabetic periods of diabetic patients were measured and compared with the daily activities of the experimental subjects, which helped generate risk factors. Fusing risk factors with DR conditions enabled early diagnosis recommendations for the experimental subjects despite the absence of any apparent symptoms. The comparison of DiabSense system outcome with clinical diagnosis reports in the experimental subjects was conducted using the A1C test. The test results confirmed the accurate assessment of early diagnosis requirements for experimental subjects by the system. Overall, DiabSense exhibits significant potential for ensuring early NIDDM treatment, improving millions of lives worldwide.
AbstractList Abstract Non-Insulin-Dependent Diabetes Mellitus (NIDDM) is a chronic health condition caused by high blood sugar levels, and if not treated early, it can lead to serious complications i.e. blindness. Human Activity Recognition (HAR) offers potential for early NIDDM diagnosis, emerging as a key application for HAR technology. This research introduces DiabSense, a state-of-the-art smartphone-dependent system for early staging of NIDDM. DiabSense incorporates HAR and Diabetic Retinopathy (DR) upon leveraging the power of two different Graph Neural Networks (GNN). HAR uses a comprehensive array of 23 human activities resembling Diabetes symptoms, and DR is a prevalent complication of NIDDM. Graph Attention Network (GAT) in HAR achieved 98.32% accuracy on sensor data, while Graph Convolutional Network (GCN) in the Aptos 2019 dataset scored 84.48%, surpassing other state-of-the-art models. The trained GCN analyzed retinal images of four experimental human subjects for DR report generation, and GAT generated their average duration of daily activities over 30 days. The daily activities in non-diabetic periods of diabetic patients were measured and compared with the daily activities of the experimental subjects, which helped generate risk factors. Fusing risk factors with DR conditions enabled early diagnosis recommendations for the experimental subjects despite the absence of any apparent symptoms. The comparison of DiabSense system outcome with clinical diagnosis reports in the experimental subjects was conducted using the A1C test. The test results confirmed the accurate assessment of early diagnosis requirements for experimental subjects by the system. Overall, DiabSense exhibits significant potential for ensuring early NIDDM treatment, improving millions of lives worldwide.
Non-Insulin-Dependent Diabetes Mellitus (NIDDM) is a chronic health condition caused by high blood sugar levels, and if not treated early, it can lead to serious complications i.e. blindness. Human Activity Recognition (HAR) offers potential for early NIDDM diagnosis, emerging as a key application for HAR technology. This research introduces DiabSense, a state-of-the-art smartphone-dependent system for early staging of NIDDM. DiabSense incorporates HAR and Diabetic Retinopathy (DR) upon leveraging the power of two different Graph Neural Networks (GNN). HAR uses a comprehensive array of 23 human activities resembling Diabetes symptoms, and DR is a prevalent complication of NIDDM. Graph Attention Network (GAT) in HAR achieved 98.32% accuracy on sensor data, while Graph Convolutional Network (GCN) in the Aptos 2019 dataset scored 84.48%, surpassing other state-of-the-art models. The trained GCN analyzed retinal images of four experimental human subjects for DR report generation, and GAT generated their average duration of daily activities over 30 days. The daily activities in non-diabetic periods of diabetic patients were measured and compared with the daily activities of the experimental subjects, which helped generate risk factors. Fusing risk factors with DR conditions enabled early diagnosis recommendations for the experimental subjects despite the absence of any apparent symptoms. The comparison of DiabSense system outcome with clinical diagnosis reports in the experimental subjects was conducted using the A1C test. The test results confirmed the accurate assessment of early diagnosis requirements for experimental subjects by the system. Overall, DiabSense exhibits significant potential for ensuring early NIDDM treatment, improving millions of lives worldwide.
ArticleNumber 103
Author Masum, Abdul Kadar Muhammad
Vergara, Manuel Masias
Hasnine, Ibrahim
Alam, Md Nuho Ul
Uddin, Jia
Samad, Md. Abdus
Ashraf, Imran
Bahadur, Erfanul Hoque
Urbano, Mercedes Briones
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Cites_doi 10.2337/dc09-1939
10.1109/ACCESS.2022.3185112
10.1056/NEJM199306103282306
10.1109/JSEN.2022.3206916
10.1007/s00521-023-09001-1
10.2337/dc10-0679
10.1038/s41598-021-95947-y
10.1109/JBHI.2019.2909688
10.1109/ISSPIT47144.2019.9001846
10.1016/j.neucom.2015.07.085
10.3389/fpsyt.2020.574375
10.1609/aaai.v32i1.11604
10.2337/dc16-1728
10.1109/SENSORS43011.2019.8956951
10.7759/cureus.56674
10.1016/j.patrec.2014.04.011
10.1046/j.1365-2125.1999.00092.x
10.1016/j.knosys.2021.106970
10.1038/s41598-020-78418-8
10.1016/S1440-2440(04)80273-1
10.1109/ICCV.2019.00936
10.3390/electronics13122290
10.1109/JSEN.2014.2370945
10.2337/dc09-9033
10.2196/jmir.2208
10.1109/ICoICT49345.2020.9166229
10.1007/s11042-019-08463-7
10.1109/ECACE.2019.8679226
10.1007/s11063-021-10491-0
10.1109/CVPR.2013.365
10.1007/s11760-020-01816-y
10.4239/wjd.v6.i3.489
10.1016/j.eswa.2022.116764
10.1056/NEJM199107183250302
10.1016/j.future.2017.11.029
10.1109/ICCVW.2011.6130379
10.1210/er.2015-1137
10.1145/2733373.2806333
10.1016/j.jbi.2014.07.009
10.3390/electronics9060914
10.1109/SURV.2012.110112.00192
10.1007/s12652-020-02727-z
10.1001/jama.2016.17216
10.1016/j.eswa.2019.04.057
10.3390/s20226670
10.1001/jamaophthalmol.2019.2004
10.1016/j.patrec.2016.01.001
10.1002/9781394242252.ch11
10.3390/electronics10182194
10.1016/0140-6736(91)90664-B
10.1109/CVPR.2018.00745
10.1016/j.asoc.2017.09.027
10.1007/s00138-021-01253-y
10.1145/3442381.3450006
10.1088/0967-3334/30/4/R01
10.3390/bios12060393
10.1007/s11760-021-01904-7
10.1016/j.inffus.2023.01.015
10.1007/978-3-540-24646-6_1
10.1007/978-981-19-5868-7_36
10.1109/ACCESS.2023.3301618
10.1109/ICOSEC58147.2023.10276227
10.1056/NEJMoa1310799
10.1016/j.eswa.2016.04.032
10.1109/ACCESS.2020.2982225
10.1159/000499541
10.1007/s00371-021-02283-3
10.1038/s41746-021-00514-4
10.1007/s00521-021-05913-y
10.1007/978-981-13-8798-2_12
10.1016/j.eswa.2018.07.053
10.1155/2016/2316757
10.1007/s00371-012-0752-6
10.2337/dc13-1507
10.1109/CVPR52688.2022.01166
10.1007/s00521-021-06431-7
10.1109/JSEN.2020.2964278
10.1007/978-3-031-33309-5_10
10.1007/978-981-15-5788-0_64
10.1016/j.eswa.2018.03.056
10.1109/IJCNN.2016.7727224
10.1038/s41598-022-11880-8
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Keywords Diabetic retinopathy
Diabetes
Human activity recognition
Graph Neural Network
NIDDM
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References Simó-ServatOHernándezCSimóRDiabetic retinopathy in the context of patients with diabetesOphthalmic Res201962421121710.1159/000499541
HanCZhangLTangYHuangWMinFHeJHuman activity recognition using wearable sensors by heterogeneous convolutional neural networksExpert Syst Appl202219811676410.1016/j.eswa.2022.116764
KingPPeacockIDonnellyRThe uk prospective diabetes study (ukpds): clinical and therapeutic implications for type 2 diabetesBr J Clin Pharmacol199948564364810.1046/j.1365-2125.1999.00092.x
Chakravarthy SS, Bharanidharan N, Kumar VV, Mahesh T, Khan SB, Almusharraf A, Albalawi E. Intelligent recognition of multimodal human activities for personal healthcare. IEEE Access 2024.
Ding X, Zhang X, Han J, Ding G. Scaling up your kernels to 31x31: Revisiting large kernel design in cnns. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022;11963–11975.
BodapatiJDShaikNSNaralasettiVDeep convolution feature aggregation: an application to diabetic retinopathy severity level predictionSIViP20211592393010.1007/s11760-020-01816-y
QuaidMAKJalalAWearable sensors based human behavioral pattern recognition using statistical features and reweighted genetic algorithmMultimedia Tools Appl20207996061608310.1007/s11042-019-08463-7
GreggEWLiYWangJRios BurrowsNAliMKRolkaDWilliamsDEGeissLChanges in diabetes-related complications in the united states, 1990–2010N Engl J Med2014370161514152310.1056/NEJMoa1310799
Shan CY, Han PY, Yin OS. Deep analysis for smartphone-based human activity recognition. In: 2020 8th International Conference on Information and Communication Technology (ICoICT), IEEE 2020;1–5.
Oono K, Suzuki T. Graph Neural Networks exponentially lose expressive power for node classification. arXiv preprint arXiv:1905.10947 2019.
UmbrichtDChengW-YLipsmeierFBamdadianALindemannMDeep learning-based human activity recognition for continuous activity and gesture monitoring for schizophrenia patients with negative symptomsFront Psych20201157437510.3389/fpsyt.2020.574375
MukhopadhyaySCWearable sensors for human activity monitoring: a reviewIEEE Sens J20141531321133010.1109/JSEN.2014.2370945
LaraODLabradorMAA survey on human activity recognition using wearable sensorsIEEE Commun Surv Tutorials20121531192120910.1109/SURV.2012.110112.00192
ChallaSKKumarASemwalVBA multibranch CNN-BILSTM model for human activity recognition using wearable sensor dataVis Comput202238124095410910.1007/s00371-021-02283-3
Li Q, Han Z, Wu X-M. Deeper insights into graph convolutional networks for semi-supervised learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, 2018;32.
Ha S, Choi S. Convolutional neural networks for human activity recognition using multiple accelerometer and gyroscope sensors. In: 2016 International Joint Conference on Neural Networks (IJCNN), IEEE 2016;381–388.
Barna A, Masum AKM, Hossain ME, Bahadur EH, Alam MS. A study on human activity recognition using gyroscope, accelerometer, temperature and humidity data. In: 2019 International Conference on Electrical, Computer and Communication Engineering (ecce), IEEE, 2019, pp. 1–6.
KhanAHammerlaNMellorSPlötzTOptimising sampling rates for accelerometer-based human activity recognitionPattern Recogn Lett201673334010.1016/j.patrec.2016.01.001
Soni V, Yadav H, Semwal VB, Roy B, Choubey DK, Mallick DK. A novel smartphone-based human activity recognition using deep learning in health care. In: Machine Learning, Image Processing, Network Security and Data Sciences: Select Proceedings of 3rd International Conference on MIND 2021, Springer 2023;493–503.
AggarwalJKXiaLHuman activity recognition from 3d data: a reviewPattern Recogn Lett201448708010.1016/j.patrec.2014.04.011
LuLZhangCCaoKDengTYangQA multichannel CNN-GRU model for human activity recognitionIEEE Access202210667976681010.1109/ACCESS.2022.3185112
GeJXuGLuJXuXMengXGraphsensor: a graph attention network for time-series sensorElectronics20241312229010.3390/electronics13122290
González PA. American Academy of Ophthalmology: How to Take Retinal Images with a Smartphone. 2020. https://www.aao.org/education/clinical-video/how-to-take-retinal-images-with-smartphone#disqus_thread].
Nematallah H, Rajan S, Cretu A-M. Logistic model tree for human activity recognition using smartphone-based inertial sensors. In: 2019 IEEE SENSORS, IEEE 2019;1–4.
Asia Pacific Tele-Ophthalmology Society: Aptos 2019 blindness detection, 2019. https://www.kaggle.com/c/aptos2019-blindness-detection/data. Accessed 1 Jan 2023
International Diabetes Federation: IDF Diabetes Atlas, 10th ed. Brussels, Belgium. 2021. https://www.diabetesatlas.org
YadavSKTiwariKPandeyHMAkbarSAA review of multimodal human activity recognition with special emphasis on classification, applications, challenges and future directionsKnowl-Based Syst202122310697010.1016/j.knosys.2021.106970
StraczkiewiczMJamesPOnnelaJ-PA systematic review of smartphone-based human activity recognition methods for health researchNPJ Dig Med20214114810.1038/s41746-021-00514-4
AlexSANayahiJJVShineHGopirekhaVDeep convolutional neural network for diabetes mellitus predictionNeural Comput Appl20223421319132710.1007/s00521-021-06431-7
AsimYAzamMAEhatisham-ul-HaqMNaeemUKhalidAContext-aware human activity recognition (CAHAR) in-the-wild using smartphone accelerometerIEEE Sens J20202084361437110.1109/JSEN.2020.2964278
NentwichMMUlbigMWDiabetic retinopathy-ocular complications of diabetes mellitusWorld J Diabetes20156348910.4239/wjd.v6.i3.489
IgnatovAReal-time human activity recognition from accelerometer data using convolutional neural networksAppl Soft Comput20186291592210.1016/j.asoc.2017.09.027
Sivaraman M, Thyagarajan M, Sumitha J. Predicting early stage disease diagnosis using machine learning algorithms. In: 2023 4th International Conference on Smart Electronics and Communication (ICOSEC), IEEE 2023;1177–1183.
WangYCangSYuHA survey on wearable sensor modality centred human activity recognition in health careExpert Syst Appl201913716719010.1016/j.eswa.2019.04.057
LorenzoCWagenknechtLEHanleyAJRewersMJKarterAJHaffnerSMA1c between 5.7 and 6.4% as a marker for identifying pre-diabetes, insulin sensitivity and secretion, and cardiovascular risk factors: the insulin resistance atherosclerosis study (iras)Diabetes Care20103392104210910.2337/dc10-0679
Wang B, Zhao D, Lioma C, Li Q, Zhang P, Simonsen JG. Encoding word order in complex embeddings. 2019. arXiv preprint arXiv:1912.12333.
CommitteeTIEInternational expert committee report on the role of the a1c assay in the diagnosis of diabetesDiabetes Care2009327132710.2337/dc09-9033
Kassani SH, Kassani PH, Khazaeinezhad R, Wesolowski MJ, Schneider KA, Deters R. Diabetic retinopathy classification using a modified xception architecture. In: 2019 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), IEEE 2019;1–6.
Wong W, Juwono FH, Capriono C. Diabetic retinopathy detection and grading: A transfer learning approach using simultaneous parameter optimization and feature-weighted ecoc ensemble. IEEE Access 2023
YinXLiuZLiuDRenXA novel CNN-based BI-LSTM parallel model with attention mechanism for human activity recognition with noisy dataSci Rep2022121787810.1038/s41598-022-11880-8
VelickovicPCucurullGCasanovaARomeroALioPBengioYGraph attention networks.Stat20171050201048550
Hu J, Shen L, Sun G. Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018;7132–7141.
ShaikNSCherukuriTKLesion-aware attention with neural support vector machine for retinopathy diagnosisMach Vis Appl202132612610.1007/s00138-021-01253-y
VishwakarmaSAgrawalAA survey on activity recognition and behavior understanding in video surveillanceVis Comput201329983100910.1007/s00371-012-0752-6
PreeceSJGoulermasJYKenneyLPHowardDMeijerKCromptonRActivity identification using body-mounted sensors-a review of classification techniquesPhysiol Meas2009304110.1088/0967-3334/30/4/R01
Google: Case Study: TensorFlow in Medicine—Retinal Imaging, TensorFlow Dev Summit 2017. 2017. https://youtu.be/oOeZ7IgEN4o?t=156
Li G, Muller M, Thabet A, Ghanem B. Deepgcns: Can gcns go as deep as CNNS? In: Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019;9267–9276.
GulshanVRajanRPWidnerKWuDWubbelsPRhodesTWhitehouseKCoramMCorradoGRamasamyKPerformance of a deep-learning algorithm vs manual grading for detecting diabetic retinopathy in IndiaJAMA Ophthalmol.201913799879310.1001/jamaophthalmol.2019.2004
Jiang W, Yin Z. Human activity recognition using wearable sensors by deep convolutional neural networks. In: Proceedings of the 23rd ACM International Conference on Multimedia, 2015;1307–1310.
BodapatiJDShaikNSNaralasettiVComposite deep neural network with gated-attention mechanism for diabetic retinopathy severity classificationJ Ambient Intell Humaniz Comput202112109825983910.1007/s12652-020-02727-z
MansonJEStampferMColditzGWillettWRosnerBHennekensCSpeizerFRimmEKrolewskiAPhysical activity and incidence of non-insulin-dependent diabetes mellitus in womenLancet1991338877077477810.1016/0140-6736(91)90664-B
Ni B, Wang G, Moulin P. Rgbd-hudaact: A color-depth video database for human daily activity recognition. In: 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), IEEE, 2011;1147–1153.
Nasir D, Bourkha MEA, Hatim A, Elbeid S, Ez-ziymy S, Zahid K. Predicting blood glucose levels in type 1 diabetes using lstm. In: Modern Artificial Intelligence and Data Science: Tools, Techniques and Systems. Springer, Cham Switzerland 2023;121–135.
WuWDasguptaSRamirezEEPetersonCNormanGJClassification accuracies of physical activities using smartphone motion sensorsJ Med Internet Res2012145220810.2196/jmir.2208
GulshanVPengLCoramMStumpeMCWuDNarayanaswamyAVenugopalanSWidnerKMadamsTCuadrosJDevelopment and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographsJAMA2016316222402241010.1001/jama.2016.17216
DinpajhouhMSeyyedsalehiSAAutomated detecting and severity grading of
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959_CR37
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References_xml – reference: BodapatiJDShaikNSNaralasettiVComposite deep neural network with gated-attention mechanism for diabetic retinopathy severity classificationJ Ambient Intell Humaniz Comput202112109825983910.1007/s12652-020-02727-z
– reference: Hu J, Shen L, Sun G. Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018;7132–7141.
– reference: MukhopadhyaySCWearable sensors for human activity monitoring: a reviewIEEE Sens J20141531321133010.1109/JSEN.2014.2370945
– reference: UmbrichtDChengW-YLipsmeierFBamdadianALindemannMDeep learning-based human activity recognition for continuous activity and gesture monitoring for schizophrenia patients with negative symptomsFront Psych20201157437510.3389/fpsyt.2020.574375
– reference: Google: Case Study: TensorFlow in Medicine—Retinal Imaging, TensorFlow Dev Summit 2017. 2017. https://youtu.be/oOeZ7IgEN4o?t=156
– reference: ColbergSRSigalRJYardleyJERiddellMCDunstanDWDempseyPCHortonESCastorinoKTateDFPhysical activity/exercise and diabetes: a position statement of the American diabetes associationDiabetes Care20163911206510.2337/dc16-1728
– reference: KingPPeacockIDonnellyRThe uk prospective diabetes study (ukpds): clinical and therapeutic implications for type 2 diabetesBr J Clin Pharmacol199948564364810.1046/j.1365-2125.1999.00092.x
– reference: Bodor R, Jackson B, Papanikolopoulos N. Vision-based human tracking and activity recognition. In: Proc. of the 11th Mediterranean Conf. on Control and Automation, Citeseer, 2003;1:1–6.
– reference: Soni V, Yadav H, Semwal VB, Roy B, Choubey DK, Mallick DK. A novel smartphone-based human activity recognition using deep learning in health care. In: Machine Learning, Image Processing, Network Security and Data Sciences: Select Proceedings of 3rd International Conference on MIND 2021, Springer 2023;493–503.
– reference: AggarwalJKXiaLHuman activity recognition from 3d data: a reviewPattern Recogn Lett201448708010.1016/j.patrec.2014.04.011
– reference: YinXLiuZLiuDRenXA novel CNN-based BI-LSTM parallel model with attention mechanism for human activity recognition with noisy dataSci Rep2022121787810.1038/s41598-022-11880-8
– reference: Asia Pacific Tele-Ophthalmology Society: Aptos 2019 blindness detection, 2019. https://www.kaggle.com/c/aptos2019-blindness-detection/data. Accessed 1 Jan 2023
– reference: BodapatiJDNaralasettiVShareefSNHakakSBilalMMaddikuntaPKRJoOBlended multi-modal deep convnet features for diabetic retinopathy severity predictionElectronics20209691410.3390/electronics9060914
– reference: LaraODLabradorMAA survey on human activity recognition using wearable sensorsIEEE Commun Surv Tutorials20121531192120910.1109/SURV.2012.110112.00192
– reference: PreeceSJGoulermasJYKenneyLPHowardDMeijerKCromptonRActivity identification using body-mounted sensors-a review of classification techniquesPhysiol Meas2009304110.1088/0967-3334/30/4/R01
– reference: GulshanVPengLCoramMStumpeMCWuDNarayanaswamyAVenugopalanSWidnerKMadamsTCuadrosJDevelopment and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographsJAMA2016316222402241010.1001/jama.2016.17216
– reference: D’AngeloGPalmieriFEnhancing COVID-19 tracking apps with human activity recognition using a deep convolutional neural network and har-imagesNeural Comput Appl20233519138611387710.1007/s00521-021-05913-y
– reference: ChouC-FSherrodCEZhangXBarkerLEBullardKMCrewsJESaaddineJBBarriers to eye care among people aged 40 years and older with diagnosed diabetes, 2006–2010Diabetes Care201437118018810.2337/dc13-1507
– reference: GreggEWLiYWangJRios BurrowsNAliMKRolkaDWilliamsDEGeissLChanges in diabetes-related complications in the united states, 1990–2010N Engl J Med2014370161514152310.1056/NEJMoa1310799
– reference: DinpajhouhMSeyyedsalehiSAAutomated detecting and severity grading of diabetic retinopathy using transfer learning and attention mechanismNeural Comput Appl20233533239592397110.1007/s00521-023-09001-1
– reference: Nasir D, Bourkha MEA, Hatim A, Elbeid S, Ez-ziymy S, Zahid K. Predicting blood glucose levels in type 1 diabetes using lstm. In: Modern Artificial Intelligence and Data Science: Tools, Techniques and Systems. Springer, Cham Switzerland 2023;121–135.
– reference: VishwakarmaSAgrawalAA survey on activity recognition and behavior understanding in video surveillanceVis Comput201329983100910.1007/s00371-012-0752-6
– reference: YadavSKTiwariKPandeyHMAkbarSAA review of multimodal human activity recognition with special emphasis on classification, applications, challenges and future directionsKnowl-Based Syst202122310697010.1016/j.knosys.2021.106970
– reference: LiXZhangJSafaraFImproving the accuracy of diabetes diagnosis applications through a hybrid feature selection algorithmNeural Process Lett202355115316910.1007/s11063-021-10491-0
– reference: QuaidMAKJalalAWearable sensors based human behavioral pattern recognition using statistical features and reweighted genetic algorithmMultimedia Tools Appl20207996061608310.1007/s11042-019-08463-7
– reference: Alsheikh MA, Selim A, Niyato D, Doyle L, Lin S, Tan H-P. Deep activity recognition models with triaxial accelerometers. 2015. arXiv preprint arXiv:1511.04664
– reference: BahadurEHMasumAKMBaruaAUddinMZActive sense: Early staging of non-insulin dependent diabetes mellitus (NIDDM) hinges upon recognizing daily activity patternElectronics202110.3390/electronics10182194
– reference: HuangJLinSWangNDaiGXieYZhouJTSE-CNN: a two-stage end-to-end CNN for human activity recognitionIEEE J Biomed Health Inform202024129229910.1109/JBHI.2019.2909688
– reference: GuidouxRDuclosMFleuryGLacommePLamaudièreNManenqP-HParisLRenLRoussetSA smartphone-driven methodology for estimating physical activities and energy expenditure in free living conditionsJ Biomed Inform20145227127810.1016/j.jbi.2014.07.009
– reference: NathanDMLong-term complications of diabetes mellitusN Engl J Med1993328231676168510.1056/NEJM199306103282306
– reference: Reyes-OrtizJ-LOnetoLSamàAParraXAnguitaDTransition-aware human activity recognition using smartphonesNeurocomputing201617175476710.1016/j.neucom.2015.07.085
– reference: BhattacharyaDSharmaDKimWIjazMFSinghPKENSEM-HAR: an ensemble deep learning model for smartphone sensor-based human activity recognition for measurement of elderly health monitoringBiosensors202212639310.3390/bios12060393
– reference: Li G, Muller M, Thabet A, Ghanem B. Deepgcns: Can gcns go as deep as CNNS? In: Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019;9267–9276.
– reference: Oono K, Suzuki T. Graph Neural Networks exponentially lose expressive power for node classification. arXiv preprint arXiv:1905.10947 2019.
– reference: StraczkiewiczMJamesPOnnelaJ-PA systematic review of smartphone-based human activity recognition methods for health researchNPJ Dig Med20214114810.1038/s41746-021-00514-4
– reference: Ding X, Zhang X, Han J, Ding G. Scaling up your kernels to 31x31: Revisiting large kernel design in cnns. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022;11963–11975.
– reference: HelmrichSPRaglandDRLeungRWPaffenbargerRSJrPhysical activity and reduced occurrence of non-insulin-dependent diabetes mellitusN Engl J Med1991325314715210.1056/NEJM199107183250302
– reference: Li Q, Han Z, Wu X-M. Deeper insights into graph convolutional networks for semi-supervised learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, 2018;32.
– reference: JalalAQuaidMAKTahirSKimKA study of accelerometer and gyroscope measurements in physical life-log activities detection systemsSensors20202022667010.3390/s20226670
– reference: Xia L, Aggarwal J. Spatio-temporal depth cuboid similarity feature for activity recognition using depth camera. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2013;2834–2841.
– reference: GeJXuGLuJXuXMengXGraphsensor: a graph attention network for time-series sensorElectronics20241312229010.3390/electronics13122290
– reference: UddinMZSoyluAHuman activity recognition using wearable sensors, discriminant analysis, and long short-term memory-based neural structured learningSci Rep20211111645510.1038/s41598-021-95947-y
– reference: NentwichMMUlbigMWDiabetic retinopathy-ocular complications of diabetes mellitusWorld J Diabetes20156348910.4239/wjd.v6.i3.489
– reference: LorenzoCWagenknechtLEHanleyAJRewersMJKarterAJHaffnerSMA1c between 5.7 and 6.4% as a marker for identifying pre-diabetes, insulin sensitivity and secretion, and cardiovascular risk factors: the insulin resistance atherosclerosis study (iras)Diabetes Care20103392104210910.2337/dc10-0679
– reference: RonaoCAChoS-BHuman activity recognition with smartphone sensors using deep learning neural networksExpert Syst Appl20165923524410.1016/j.eswa.2016.04.032
– reference: IslamMMNooruddinSKarrayFMuhammadGMulti-level feature fusion for multimodal human activity recognition in internet of healthcare thingsInfo Fusion202394173110.1016/j.inffus.2023.01.015
– reference: Li C, Niu D, Jiang B, Zuo X, Yang J. Meta-har: Federated representation learning for human activity recognition. In: Proceedings of the Web Conference 2021, 2021;912–922.
– reference: Chakravarthy SS, Bharanidharan N, Kumar VV, Mahesh T, Khan SB, Almusharraf A, Albalawi E. Intelligent recognition of multimodal human activities for personal healthcare. IEEE Access 2024.
– reference: WangYCangSYuHA survey on wearable sensor modality centred human activity recognition in health careExpert Syst Appl201913716719010.1016/j.eswa.2019.04.057
– reference: González PA. American Academy of Ophthalmology: How to Take Retinal Images with a Smartphone. 2020. https://www.aao.org/education/clinical-video/how-to-take-retinal-images-with-smartphone#disqus_thread].
– reference: Barna A, Masum AKM, Hossain ME, Bahadur EH, Alam MS. A study on human activity recognition using gyroscope, accelerometer, temperature and humidity data. In: 2019 International Conference on Electrical, Computer and Communication Engineering (ecce), IEEE, 2019, pp. 1–6.
– reference: Nematallah H, Rajan S, Cretu A-M. Logistic model tree for human activity recognition using smartphone-based inertial sensors. In: 2019 IEEE SENSORS, IEEE 2019;1–4.
– reference: ChallaSKKumarASemwalVBA multibranch CNN-BILSTM model for human activity recognition using wearable sensor dataVis Comput202238124095410910.1007/s00371-021-02283-3
– reference: Wong W, Juwono FH, Capriono C. Diabetic retinopathy detection and grading: A transfer learning approach using simultaneous parameter optimization and feature-weighted ecoc ensemble. IEEE Access 2023
– reference: BodapatiJDShaikNSNaralasettiVDeep convolution feature aggregation: an application to diabetic retinopathy severity level predictionSIViP20211592393010.1007/s11760-020-01816-y
– reference: MansonJEStampferMColditzGWillettWRosnerBHennekensCSpeizerFRimmEKrolewskiAPhysical activity and incidence of non-insulin-dependent diabetes mellitus in womenLancet1991338877077477810.1016/0140-6736(91)90664-B
– reference: Gangwar AK, Ravi V. Diabetic retinopathy detection using transfer learning and deep learning. In: Evolution in computational intelligence: frontiers in intelligent computing: theory and applications (FICTA 2020), 2021:1;679–689. Springer
– reference: HassanMMUddinMZMohamedAAlmogrenAA robust human activity recognition system using smartphone sensors and deep learningFutur Gener Comput Syst20188130731310.1016/j.future.2017.11.029
– reference: AlexSANayahiJJVShineHGopirekhaVDeep convolutional neural network for diabetes mellitus predictionNeural Comput Appl20223421319132710.1007/s00521-021-06431-7
– reference: IgnatovAReal-time human activity recognition from accelerometer data using convolutional neural networksAppl Soft Comput20186291592210.1016/j.asoc.2017.09.027
– reference: Wang B, Zhao D, Lioma C, Li Q, Zhang P, Simonsen JG. Encoding word order in complex embeddings. 2019. arXiv preprint arXiv:1912.12333.
– reference: Bao L, Intille SS. Activity recognition from user-annotated acceleration data. In: International Conference on Pervasive Computing, Springer, 2004;1–17.
– reference: GulshanVRajanRPWidnerKWuDWubbelsPRhodesTWhitehouseKCoramMCorradoGRamasamyKPerformance of a deep-learning algorithm vs manual grading for detecting diabetic retinopathy in IndiaJAMA Ophthalmol.201913799879310.1001/jamaophthalmol.2019.2004
– reference: VelickovicPCucurullGCasanovaARomeroALioPBengioYGraph attention networks.Stat20171050201048550
– reference: AdemKExudate detection for diabetic retinopathy with circular hough transformation and convolutional neural networksExpert Syst Appl201811428929510.1016/j.eswa.2018.07.053
– reference: Jiang W, Yin Z. Human activity recognition using wearable sensors by deep convolutional neural networks. In: Proceedings of the 23rd ACM International Conference on Multimedia, 2015;1307–1310.
– reference: KumarGChatterjeeSChattopadhyayCDristi: a hybrid deep neural network for diabetic retinopathy diagnosisSIViP20211581679168610.1007/s11760-021-01904-7
– reference: CommitteeTIEInternational expert committee report on the role of the a1c assay in the diagnosis of diabetesDiabetes Care2009327132710.2337/dc09-9033
– reference: Ha S, Choi S. Convolutional neural networks for human activity recognition using multiple accelerometer and gyroscope sensors. In: 2016 International Joint Conference on Neural Networks (IJCNN), IEEE 2016;381–388.
– reference: Ni B, Wang G, Moulin P. Rgbd-hudaact: A color-depth video database for human daily activity recognition. In: 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), IEEE, 2011;1147–1153.
– reference: Islam M, Ferdousi R, Rahman S, Bushra HY. Likelihood prediction of diabetes at early stage using data mining techniques. In: Computer Vision and Machine Intelligence in Medical Image Analysis, Springer, Cham Switzerland 2020;113–125.
– reference: KhanAHammerlaNMellorSPlötzTOptimising sampling rates for accelerometer-based human activity recognitionPattern Recogn Lett201673334010.1016/j.patrec.2016.01.001
– reference: PhukanNMohineSMondalAManikandanMSPachoriRBConvolutional neural network-based human activity recognition for edge fitness and context-aware health monitoring devicesIEEE Sens J20222222218162182610.1109/JSEN.2022.3206916
– reference: Sivaraman M, Thyagarajan M, Sumitha J. Predicting early stage disease diagnosis using machine learning algorithms. In: 2023 4th International Conference on Smart Electronics and Communication (ICOSEC), IEEE 2023;1177–1183.
– reference: XiaKHuangJWangHLstm-cnn architecture for human activity recognitionIEEE Access20208568555686610.1109/ACCESS.2020.2982225
– reference: Shan CY, Han PY, Yin OS. Deep analysis for smartphone-based human activity recognition. In: 2020 8th International Conference on Information and Communication Technology (ICoICT), IEEE 2020;1–5.
– reference: International Diabetes Federation: IDF Diabetes Atlas, 10th ed. Brussels, Belgium. 2021. https://www.diabetesatlas.org
– reference: NwekeHFTehYWAl-GaradiMAAloURDeep learning algorithms for human activity recognition using mobile and wearable sensor networks: state of the art and research challengesExpert Syst Appl201810523326110.1016/j.eswa.2018.03.056
– reference: HanKWangYGuoJTangYWuEVision GNN: an image is worth graph of nodesAdv Neural Inf Process Syst20223582918303
– reference: AsimYAzamMAEhatisham-ul-HaqMNaeemUKhalidAContext-aware human activity recognition (CAHAR) in-the-wild using smartphone accelerometerIEEE Sens J20202084361437110.1109/JSEN.2020.2964278
– reference: ZhangXGreggEWWilliamsonDFBarkerLEThomasWBullardKMImperatoreGWilliamsDEAlbrightALA1c level and future risk of diabetes: a systematic reviewDiabetes Care20103371665167310.2337/dc09-1939
– reference: Kulkarni A, Thool AR, Daigavane S. Understanding the clinical relationship between diabetic retinopathy, nephropathy, and neuropathy: a comprehensive review. Cureus 2024;16(3).
– reference: PapadopoulosAIakovakisDKlingelhoeferLBostantjopoulouSChaudhuriKRKyritsisKHadjidimitriouSCharisisVHadjileontiadisLJDelopoulosAUnobtrusive detection of Parkinson’s disease from multi-modal and in-the-wild sensor data using deep learning techniquesSci Rep20201012137010.1038/s41598-020-78418-8
– reference: LuLZhangCCaoKDengTYangQA multichannel CNN-GRU model for human activity recognitionIEEE Access202210667976681010.1109/ACCESS.2022.3185112
– reference: BaumanAEUpdating the evidence that physical activity is good for health: an epidemiological review 2000–2003J Sci Med Sport20047161910.1016/S1440-2440(04)80273-1
– reference: WuWDasguptaSRamirezEEPetersonCNormanGJClassification accuracies of physical activities using smartphone motion sensorsJ Med Internet Res2012145220810.2196/jmir.2208
– reference: Simó-ServatOHernándezCSimóRDiabetic retinopathy in the context of patients with diabetesOphthalmic Res201962421121710.1159/000499541
– reference: Lee J, Kim J et al. Energy-efficient real-time human activity recognition on smart mobile devices. Mobile Information Systems 2016;2016.
– reference: Kautzky-WillerAHarreiterJPaciniGSex and gender differences in risk, pathophysiology and complications of type 2 diabetes mellitusEndocr Rev201637327831610.1210/er.2015-1137
– reference: Kaur C, Al Ansari MS, Dwivedi VK, Suganthi D. Implementation of a neuro-fuzzy-based classifier for the detection of types 1 and 2 diabetes. Advances in Fuzzy-Based Internet of Medical Things (IoMT), 2024;163–178.
– reference: ShaikNSCherukuriTKLesion-aware attention with neural support vector machine for retinopathy diagnosisMach Vis Appl202132612610.1007/s00138-021-01253-y
– reference: Kassani SH, Kassani PH, Khazaeinezhad R, Wesolowski MJ, Schneider KA, Deters R. Diabetic retinopathy classification using a modified xception architecture. In: 2019 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), IEEE 2019;1–6.
– reference: HanCZhangLTangYHuangWMinFHeJHuman activity recognition using wearable sensors by heterogeneous convolutional neural networksExpert Syst Appl202219811676410.1016/j.eswa.2022.116764
– volume: 33
  start-page: 1665
  issue: 7
  year: 2010
  ident: 959_CR77
  publication-title: Diabetes Care
  doi: 10.2337/dc09-1939
– volume: 10
  start-page: 66797
  year: 2022
  ident: 959_CR92
  publication-title: IEEE Access
  doi: 10.1109/ACCESS.2022.3185112
– volume: 328
  start-page: 1676
  issue: 23
  year: 1993
  ident: 959_CR44
  publication-title: N Engl J Med
  doi: 10.1056/NEJM199306103282306
– ident: 959_CR68
– volume: 22
  start-page: 21816
  issue: 22
  year: 2022
  ident: 959_CR59
  publication-title: IEEE Sens J
  doi: 10.1109/JSEN.2022.3206916
– volume: 35
  start-page: 23959
  issue: 33
  year: 2023
  ident: 959_CR63
  publication-title: Neural Comput Appl
  doi: 10.1007/s00521-023-09001-1
– volume: 33
  start-page: 2104
  issue: 9
  year: 2010
  ident: 959_CR76
  publication-title: Diabetes Care
  doi: 10.2337/dc10-0679
– volume: 11
  start-page: 16455
  issue: 1
  year: 2021
  ident: 959_CR25
  publication-title: Sci Rep
  doi: 10.1038/s41598-021-95947-y
– volume: 24
  start-page: 292
  issue: 1
  year: 2020
  ident: 959_CR89
  publication-title: IEEE J Biomed Health Inform
  doi: 10.1109/JBHI.2019.2909688
– ident: 959_CR49
  doi: 10.1109/ISSPIT47144.2019.9001846
– volume: 171
  start-page: 754
  year: 2016
  ident: 959_CR27
  publication-title: Neurocomputing
  doi: 10.1016/j.neucom.2015.07.085
– volume: 11
  start-page: 574375
  year: 2020
  ident: 959_CR40
  publication-title: Front Psych
  doi: 10.3389/fpsyt.2020.574375
– ident: 959_CR67
  doi: 10.1609/aaai.v32i1.11604
– volume: 39
  start-page: 2065
  issue: 11
  year: 2016
  ident: 959_CR8
  publication-title: Diabetes Care
  doi: 10.2337/dc16-1728
– ident: 959_CR87
  doi: 10.1109/SENSORS43011.2019.8956951
– ident: 959_CR93
  doi: 10.7759/cureus.56674
– ident: 959_CR1
– volume: 48
  start-page: 70
  year: 2014
  ident: 959_CR20
  publication-title: Pattern Recogn Lett
  doi: 10.1016/j.patrec.2014.04.011
– volume: 48
  start-page: 643
  issue: 5
  year: 1999
  ident: 959_CR3
  publication-title: Br J Clin Pharmacol
  doi: 10.1046/j.1365-2125.1999.00092.x
– volume: 223
  start-page: 106970
  year: 2021
  ident: 959_CR11
  publication-title: Knowl-Based Syst
  doi: 10.1016/j.knosys.2021.106970
– volume: 10
  start-page: 21370
  issue: 1
  year: 2020
  ident: 959_CR42
  publication-title: Sci Rep
  doi: 10.1038/s41598-020-78418-8
– volume: 7
  start-page: 6
  issue: 1
  year: 2004
  ident: 959_CR6
  publication-title: J Sci Med Sport
  doi: 10.1016/S1440-2440(04)80273-1
– ident: 959_CR66
  doi: 10.1109/ICCV.2019.00936
– volume: 13
  start-page: 2290
  issue: 12
  year: 2024
  ident: 959_CR69
  publication-title: Electronics
  doi: 10.3390/electronics13122290
– volume: 15
  start-page: 1321
  issue: 3
  year: 2014
  ident: 959_CR21
  publication-title: IEEE Sens J
  doi: 10.1109/JSEN.2014.2370945
– volume: 32
  start-page: 1327
  issue: 7
  year: 2009
  ident: 959_CR75
  publication-title: Diabetes Care
  doi: 10.2337/dc09-9033
– volume: 14
  start-page: 2208
  issue: 5
  year: 2012
  ident: 959_CR36
  publication-title: J Med Internet Res
  doi: 10.2196/jmir.2208
– ident: 959_CR73
– ident: 959_CR85
  doi: 10.1109/ICoICT49345.2020.9166229
– volume: 79
  start-page: 6061
  issue: 9
  year: 2020
  ident: 959_CR86
  publication-title: Multimedia Tools Appl
  doi: 10.1007/s11042-019-08463-7
– ident: 959_CR16
  doi: 10.1109/ECACE.2019.8679226
– ident: 959_CR17
– volume: 55
  start-page: 153
  issue: 1
  year: 2023
  ident: 959_CR81
  publication-title: Neural Process Lett
  doi: 10.1007/s11063-021-10491-0
– ident: 959_CR19
  doi: 10.1109/CVPR.2013.365
– ident: 959_CR65
– volume: 15
  start-page: 923
  year: 2021
  ident: 959_CR84
  publication-title: SIViP
  doi: 10.1007/s11760-020-01816-y
– volume: 1050
  start-page: 10
  issue: 20
  year: 2017
  ident: 959_CR38
  publication-title: Stat
– volume: 6
  start-page: 489
  issue: 3
  year: 2015
  ident: 959_CR5
  publication-title: World J Diabetes
  doi: 10.4239/wjd.v6.i3.489
– volume: 198
  start-page: 116764
  year: 2022
  ident: 959_CR12
  publication-title: Expert Syst Appl
  doi: 10.1016/j.eswa.2022.116764
– volume: 325
  start-page: 147
  issue: 3
  year: 1991
  ident: 959_CR7
  publication-title: N Engl J Med
  doi: 10.1056/NEJM199107183250302
– volume: 81
  start-page: 307
  year: 2018
  ident: 959_CR28
  publication-title: Futur Gener Comput Syst
  doi: 10.1016/j.future.2017.11.029
– ident: 959_CR18
  doi: 10.1109/ICCVW.2011.6130379
– volume: 37
  start-page: 278
  issue: 3
  year: 2016
  ident: 959_CR74
  publication-title: Endocr Rev
  doi: 10.1210/er.2015-1137
– ident: 959_CR34
– ident: 959_CR23
  doi: 10.1145/2733373.2806333
– volume: 52
  start-page: 271
  year: 2014
  ident: 959_CR29
  publication-title: J Biomed Inform
  doi: 10.1016/j.jbi.2014.07.009
– volume: 9
  start-page: 914
  issue: 6
  year: 2020
  ident: 959_CR50
  publication-title: Electronics
  doi: 10.3390/electronics9060914
– volume: 15
  start-page: 1192
  issue: 3
  year: 2012
  ident: 959_CR24
  publication-title: IEEE Commun Surv Tutorials
  doi: 10.1109/SURV.2012.110112.00192
– volume: 12
  start-page: 9825
  issue: 10
  year: 2021
  ident: 959_CR51
  publication-title: J Ambient Intell Humaniz Comput
  doi: 10.1007/s12652-020-02727-z
– volume: 316
  start-page: 2402
  issue: 22
  year: 2016
  ident: 959_CR46
  publication-title: JAMA
  doi: 10.1001/jama.2016.17216
– volume: 137
  start-page: 167
  year: 2019
  ident: 959_CR13
  publication-title: Expert Syst Appl
  doi: 10.1016/j.eswa.2019.04.057
– volume: 20
  start-page: 6670
  issue: 22
  year: 2020
  ident: 959_CR88
  publication-title: Sensors
  doi: 10.3390/s20226670
– volume: 137
  start-page: 987
  issue: 9
  year: 2019
  ident: 959_CR47
  publication-title: JAMA Ophthalmol.
  doi: 10.1001/jamaophthalmol.2019.2004
– ident: 959_CR72
– volume: 73
  start-page: 33
  year: 2016
  ident: 959_CR32
  publication-title: Pattern Recogn Lett
  doi: 10.1016/j.patrec.2016.01.001
– ident: 959_CR79
  doi: 10.1002/9781394242252.ch11
– year: 2021
  ident: 959_CR15
  publication-title: Electronics
  doi: 10.3390/electronics10182194
– volume: 338
  start-page: 774
  issue: 8770
  year: 1991
  ident: 959_CR9
  publication-title: Lancet
  doi: 10.1016/0140-6736(91)90664-B
– ident: 959_CR71
  doi: 10.1109/CVPR.2018.00745
– volume: 62
  start-page: 915
  year: 2018
  ident: 959_CR31
  publication-title: Appl Soft Comput
  doi: 10.1016/j.asoc.2017.09.027
– volume: 32
  start-page: 126
  issue: 6
  year: 2021
  ident: 959_CR83
  publication-title: Mach Vis Appl
  doi: 10.1007/s00138-021-01253-y
– ident: 959_CR55
  doi: 10.1145/3442381.3450006
– volume: 30
  start-page: 1
  issue: 4
  year: 2009
  ident: 959_CR43
  publication-title: Physiol Meas
  doi: 10.1088/0967-3334/30/4/R01
– volume: 12
  start-page: 393
  issue: 6
  year: 2022
  ident: 959_CR57
  publication-title: Biosensors
  doi: 10.3390/bios12060393
– ident: 959_CR10
– volume: 15
  start-page: 1679
  issue: 8
  year: 2021
  ident: 959_CR64
  publication-title: SIViP
  doi: 10.1007/s11760-021-01904-7
– volume: 94
  start-page: 17
  year: 2023
  ident: 959_CR41
  publication-title: Info Fusion
  doi: 10.1016/j.inffus.2023.01.015
– ident: 959_CR37
  doi: 10.1007/978-3-540-24646-6_1
– ident: 959_CR56
  doi: 10.1007/978-981-19-5868-7_36
– ident: 959_CR62
  doi: 10.1109/ACCESS.2023.3301618
– ident: 959_CR80
  doi: 10.1109/ICOSEC58147.2023.10276227
– volume: 370
  start-page: 1514
  issue: 16
  year: 2014
  ident: 959_CR2
  publication-title: N Engl J Med
  doi: 10.1056/NEJMoa1310799
– volume: 59
  start-page: 235
  year: 2016
  ident: 959_CR26
  publication-title: Expert Syst Appl
  doi: 10.1016/j.eswa.2016.04.032
– volume: 35
  start-page: 8291
  year: 2022
  ident: 959_CR53
  publication-title: Adv Neural Inf Process Syst
– volume: 8
  start-page: 56855
  year: 2020
  ident: 959_CR35
  publication-title: IEEE Access
  doi: 10.1109/ACCESS.2020.2982225
– volume: 62
  start-page: 211
  issue: 4
  year: 2019
  ident: 959_CR4
  publication-title: Ophthalmic Res
  doi: 10.1159/000499541
– volume: 38
  start-page: 4095
  issue: 12
  year: 2022
  ident: 959_CR91
  publication-title: Vis Comput
  doi: 10.1007/s00371-021-02283-3
– volume: 4
  start-page: 148
  issue: 1
  year: 2021
  ident: 959_CR39
  publication-title: NPJ Dig Med
  doi: 10.1038/s41746-021-00514-4
– volume: 35
  start-page: 13861
  issue: 19
  year: 2023
  ident: 959_CR58
  publication-title: Neural Comput Appl
  doi: 10.1007/s00521-021-05913-y
– ident: 959_CR78
  doi: 10.1007/978-981-13-8798-2_12
– volume: 114
  start-page: 289
  year: 2018
  ident: 959_CR52
  publication-title: Expert Syst Appl
  doi: 10.1016/j.eswa.2018.07.053
– ident: 959_CR54
  doi: 10.1155/2016/2316757
– volume: 29
  start-page: 983
  year: 2013
  ident: 959_CR14
  publication-title: Vis Comput
  doi: 10.1007/s00371-012-0752-6
– volume: 37
  start-page: 180
  issue: 1
  year: 2014
  ident: 959_CR45
  publication-title: Diabetes Care
  doi: 10.2337/dc13-1507
– ident: 959_CR70
  doi: 10.1109/CVPR52688.2022.01166
– volume: 34
  start-page: 1319
  issue: 2
  year: 2022
  ident: 959_CR82
  publication-title: Neural Comput Appl
  doi: 10.1007/s00521-021-06431-7
– ident: 959_CR60
– volume: 20
  start-page: 4361
  issue: 8
  year: 2020
  ident: 959_CR33
  publication-title: IEEE Sens J
  doi: 10.1109/JSEN.2020.2964278
– ident: 959_CR61
  doi: 10.1007/978-3-031-33309-5_10
– ident: 959_CR48
  doi: 10.1007/978-981-15-5788-0_64
– volume: 105
  start-page: 233
  year: 2018
  ident: 959_CR22
  publication-title: Expert Syst Appl
  doi: 10.1016/j.eswa.2018.03.056
– ident: 959_CR30
  doi: 10.1109/IJCNN.2016.7727224
– volume: 12
  start-page: 7878
  issue: 1
  year: 2022
  ident: 959_CR90
  publication-title: Sci Rep
  doi: 10.1038/s41598-022-11880-8
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Snippet Non-Insulin-Dependent Diabetes Mellitus (NIDDM) is a chronic health condition caused by high blood sugar levels, and if not treated early, it can lead to...
Abstract Non-Insulin-Dependent Diabetes Mellitus (NIDDM) is a chronic health condition caused by high blood sugar levels, and if not treated early, it can lead...
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SubjectTerms Acknowledgment
Activities of daily living
Artificial neural networks
Big Data
Big Data and Artificial Intelligence in Emerging Scientific Fields
Blindness
Chronic illnesses
Communications Engineering
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Database Management
Diabetes
Diabetes mellitus
Diabetic retinopathy
Diagnosis
Diagnostic tests
Graph Neural Network
Graph neural networks
Human activity recognition
Humans
Information Storage and Retrieval
Insulin
Mathematical Applications in Computer Science
Medical diagnosis
Networks
Neural networks
NIDDM
Patients
Recognition
Research subjects
Retinal images
Retinopathy
Risk factors
Smartphones
Symptoms
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Title DiabSense: early diagnosis of non-insulin-dependent diabetes mellitus using smartphone-based human activity recognition and diabetic retinopathy analysis with Graph Neural Network
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Volume 11
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