Latent Feature‐Based Type 2 Diabetes Prediction Using a Hybrid Stacked Sparse Autoencoder and Machine Learning Models
ABSTRACT Early and precise prediction of Type 2 diabetes is vital for effective intervention. However, extracting meaningful insights from high‐dimensional datasets with sparse values remains challenging. Sparsity and redundant features often hinder traditional machine learning algorithms' abil...
Uloženo v:
| Vydáno v: | Engineering reports (Hoboken, N.J.) Ročník 7; číslo 9 |
|---|---|
| Hlavní autoři: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Hoboken, USA
John Wiley & Sons, Inc
01.09.2025
Wiley |
| Témata: | |
| ISSN: | 2577-8196, 2577-8196 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | ABSTRACT
Early and precise prediction of Type 2 diabetes is vital for effective intervention. However, extracting meaningful insights from high‐dimensional datasets with sparse values remains challenging. Sparsity and redundant features often hinder traditional machine learning algorithms' ability to identify informative patterns. While conventional Stacked Sparse Autoencoders (SSAE) can capture key features in dense data, they typically struggle with high‐dimensional sparse data, reducing classification accuracy. To address this limitation, the study proposes a Hybrid Stacked Sparse Autoencoder (HSSAE) algorithm designed for robust feature extraction and classification in sparse data environments. The architecture incorporates L1 and L2 regularization within a binary cross‐entropy loss and employs dropout and batch normalization to improve generalization and training stability. The HSSAE algorithm's performance was tested with a sigmoid classifier and various machine learning techniques. When combined with a sigmoid layer, the model achieved 89% accuracy and an F1 score of 0.89. It also outperformed baseline models when integrated with traditional classifiers; notably, the HSSAE + K‐Nearest Neighbor (KNN) achieved an F1 score of 0.91, a recall of 0.98, 90% accuracy, and the lowest hamming loss of 0.10. Comparative evaluations included baseline classifiers like Logistic Regression (LR), KNNs, Naïve Bayes (NB), AdaBoost, and XGBoost, applied directly to the preprocessed dataset. An ablation study tested these classifiers on features extracted via the SSAE. In both cases, the HSSAE algorithm showed superior performance across all metrics. These findings demonstrate the HSSAE algorithm's effectiveness in extracting discriminative features from sparse, high‐dimensional data, emphasizing its potential for clinical decision support systems requiring high accuracy and reliability.
Overview of the proposed Hybrid Stacked Sparse Autoencoder (HSSAE) framework. |
|---|---|
| AbstractList | ABSTRACT
Early and precise prediction of Type 2 diabetes is vital for effective intervention. However, extracting meaningful insights from high‐dimensional datasets with sparse values remains challenging. Sparsity and redundant features often hinder traditional machine learning algorithms' ability to identify informative patterns. While conventional Stacked Sparse Autoencoders (SSAE) can capture key features in dense data, they typically struggle with high‐dimensional sparse data, reducing classification accuracy. To address this limitation, the study proposes a Hybrid Stacked Sparse Autoencoder (HSSAE) algorithm designed for robust feature extraction and classification in sparse data environments. The architecture incorporates L1 and L2 regularization within a binary cross‐entropy loss and employs dropout and batch normalization to improve generalization and training stability. The HSSAE algorithm's performance was tested with a sigmoid classifier and various machine learning techniques. When combined with a sigmoid layer, the model achieved 89% accuracy and an F1 score of 0.89. It also outperformed baseline models when integrated with traditional classifiers; notably, the HSSAE + K‐Nearest Neighbor (KNN) achieved an F1 score of 0.91, a recall of 0.98, 90% accuracy, and the lowest hamming loss of 0.10. Comparative evaluations included baseline classifiers like Logistic Regression (LR), KNNs, Naïve Bayes (NB), AdaBoost, and XGBoost, applied directly to the preprocessed dataset. An ablation study tested these classifiers on features extracted via the SSAE. In both cases, the HSSAE algorithm showed superior performance across all metrics. These findings demonstrate the HSSAE algorithm's effectiveness in extracting discriminative features from sparse, high‐dimensional data, emphasizing its potential for clinical decision support systems requiring high accuracy and reliability.
Overview of the proposed Hybrid Stacked Sparse Autoencoder (HSSAE) framework. ABSTRACT Early and precise prediction of Type 2 diabetes is vital for effective intervention. However, extracting meaningful insights from high‐dimensional datasets with sparse values remains challenging. Sparsity and redundant features often hinder traditional machine learning algorithms' ability to identify informative patterns. While conventional Stacked Sparse Autoencoders (SSAE) can capture key features in dense data, they typically struggle with high‐dimensional sparse data, reducing classification accuracy. To address this limitation, the study proposes a Hybrid Stacked Sparse Autoencoder (HSSAE) algorithm designed for robust feature extraction and classification in sparse data environments. The architecture incorporates L1 and L2 regularization within a binary cross‐entropy loss and employs dropout and batch normalization to improve generalization and training stability. The HSSAE algorithm's performance was tested with a sigmoid classifier and various machine learning techniques. When combined with a sigmoid layer, the model achieved 89% accuracy and an F1 score of 0.89. It also outperformed baseline models when integrated with traditional classifiers; notably, the HSSAE + K‐Nearest Neighbor (KNN) achieved an F1 score of 0.91, a recall of 0.98, 90% accuracy, and the lowest hamming loss of 0.10. Comparative evaluations included baseline classifiers like Logistic Regression (LR), KNNs, Naïve Bayes (NB), AdaBoost, and XGBoost, applied directly to the preprocessed dataset. An ablation study tested these classifiers on features extracted via the SSAE. In both cases, the HSSAE algorithm showed superior performance across all metrics. These findings demonstrate the HSSAE algorithm's effectiveness in extracting discriminative features from sparse, high‐dimensional data, emphasizing its potential for clinical decision support systems requiring high accuracy and reliability. Early and precise prediction of Type 2 diabetes is vital for effective intervention. However, extracting meaningful insights from high‐dimensional datasets with sparse values remains challenging. Sparsity and redundant features often hinder traditional machine learning algorithms' ability to identify informative patterns. While conventional Stacked Sparse Autoencoders (SSAE) can capture key features in dense data, they typically struggle with high‐dimensional sparse data, reducing classification accuracy. To address this limitation, the study proposes a Hybrid Stacked Sparse Autoencoder (HSSAE) algorithm designed for robust feature extraction and classification in sparse data environments. The architecture incorporates L1 and L2 regularization within a binary cross‐entropy loss and employs dropout and batch normalization to improve generalization and training stability. The HSSAE algorithm's performance was tested with a sigmoid classifier and various machine learning techniques. When combined with a sigmoid layer, the model achieved 89% accuracy and an F 1 score of 0.89. It also outperformed baseline models when integrated with traditional classifiers; notably, the HSSAE + K‐Nearest Neighbor (KNN) achieved an F 1 score of 0.91, a recall of 0.98, 90% accuracy, and the lowest hamming loss of 0.10. Comparative evaluations included baseline classifiers like Logistic Regression (LR), KNNs, Naïve Bayes (NB), AdaBoost, and XGBoost, applied directly to the preprocessed dataset. An ablation study tested these classifiers on features extracted via the SSAE. In both cases, the HSSAE algorithm showed superior performance across all metrics. These findings demonstrate the HSSAE algorithm's effectiveness in extracting discriminative features from sparse, high‐dimensional data, emphasizing its potential for clinical decision support systems requiring high accuracy and reliability. |
| Author | Zubair, Muhammad Khan, Iliyas Karim Sokkalingam, Rajalingam Mahmood, Zafar Abdussamad Daud, Hanita |
| Author_xml | – sequence: 1 orcidid: 0009-0005-7832-7235 surname: Abdussamad fullname: Abdussamad email: abdussamad_22009779@utp.edu.my organization: Universiti Teknologi PETRONAS – sequence: 2 givenname: Hanita surname: Daud fullname: Daud, Hanita organization: Universiti Teknologi PETRONAS – sequence: 3 givenname: Rajalingam surname: Sokkalingam fullname: Sokkalingam, Rajalingam organization: Universiti Teknologi PETRONAS – sequence: 4 givenname: Muhammad surname: Zubair fullname: Zubair, Muhammad organization: Universiti Teknologi PETRONAS – sequence: 5 givenname: Iliyas Karim surname: Khan fullname: Khan, Iliyas Karim organization: Universiti Teknologi PETRONAS – sequence: 6 givenname: Zafar surname: Mahmood fullname: Mahmood, Zafar organization: University of Gujrat |
| BookMark | eNp9kc9OAjEQxhuDiYhcfIKeTdD-WdrliApogn8S4dzMtrNYxC5p1xhuPoLP6JO4gDGePM1k5vt-mcl3TFqhCkjIKWfnnDFxgWEhzjWT_fyAtEVf617OB6r1pz8i3ZSWrBFzzZlkbfI-hRpDTccI9VvEr4_PS0jo6GyzRirotYcCa0z0MaLztvZVoPPkw4ICvdkU0Tv6VIN9aRxPa4gJ6fCtrjDYymGkEBy9A_vsA9IpQgxb412zWqUTcljCKmH3p3bIfDyaXd30pg-T26vhtGelVHlztIOyb12hUeZKCi2Yc1hk2mnghR1oC5LpgVCF5H2pZWmt1s2nTgFkyknZIbd7rqtgadbRv0LcmAq82Q2quDAQa29XaCzmmQClLOc6y1g2AKek5JznJVoptqyzPcvGKqWI5S-PM7NNwGwTMLsEGjHfi9_9Cjf_KM3ofiL2nm-wx4on |
| Cites_doi | 10.1126/science.1127647 10.54254/2755‐2721/32/20230214 10.1007/s10462‐023‐10466‐8 10.1016/j.bspc.2024.106501 10.1109/ICASSP40776.2020.9054412 10.1016/j.cegh.2018.12.004 10.1016/j.cmpb.2021.105968 10.1038/srep26094 10.1186/s13098‐021‐00767‐9 10.1109/ICAIT61638.2024.10690358 10.5152/TurkArchPediatr.2025.24183 10.1038/s41598‐025‐87992‐8 10.1016/j.dscb.2024.100135 10.3389/fdgth.2025.1557467 10.1016/j.compbiomed.2024.108734 10.1007/s10462-023-10662-6 10.58445/rars.546 10.3934/math.20241222 10.3934/publichealth.2024004 10.1186/s12916‐022‐02438‐6 10.1109/JBHI.2025.3578419 10.2337/dc25-S002 10.31782/IJCRR.2021.13127 10.1145/3615366.3615376 10.1109/ICICoS62600.2024.10636871 10.37934/ard.129.1.6074 10.1016/j.health.2022.100118 10.1007/s11063‐018‐9894‐5 10.1016/j.diabres.2021.109119 10.1109/ICCCNT61001.2024.10724390 10.3390/app14052132 10.1007/s00521‐021‐06431‐7 |
| ContentType | Journal Article |
| Copyright | 2025 The Author(s). published by John Wiley & Sons Ltd. |
| Copyright_xml | – notice: 2025 The Author(s). published by John Wiley & Sons Ltd. |
| DBID | 24P AAYXX CITATION DOA |
| DOI | 10.1002/eng2.70358 |
| DatabaseName | Wiley Open Access CrossRef Directory of Open Access Journals |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | CrossRef |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: 24P name: Wiley Online Library Open Access url: https://authorservices.wiley.com/open-science/open-access/browse-journals.html sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 2577-8196 |
| EndPage | n/a |
| ExternalDocumentID | oai_doaj_org_article_ce842a66c11744049ad6331118fec323 10_1002_eng2_70358 ENG270358 |
| Genre | researchArticle |
| GrantInformation_xml | – fundername: YUTP‐FRG funderid: 015LC0‐442 |
| GroupedDBID | 0R~ 1OC 24P AAMMB ABJCF ACCMX ACXQS ADKYN ADMLS ADZMN AEFGJ AFKRA AGXDD AIDQK AIDYY ALMA_UNASSIGNED_HOLDINGS ARCSS AVUZU BENPR BGLVJ CCPQU EBS EJD GROUPED_DOAJ HCIFZ IAO IGS ITC M7S M~E OK1 PHGZM PHGZT PIMPY PQGLB PTHSS PUEGO WIN AAYXX AFFHD ALUQN CITATION |
| ID | FETCH-LOGICAL-c3368-81daf5cdb7e38632720ddeb47d7a1bc97ca307926b315373fcc77819d6aa46d33 |
| IEDL.DBID | 24P |
| ISICitedReferencesCount | 1 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001585376600021&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2577-8196 |
| IngestDate | Fri Oct 03 12:50:59 EDT 2025 Sat Nov 29 07:07:06 EST 2025 Fri Sep 26 10:30:36 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 9 |
| Language | English |
| License | Attribution |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c3368-81daf5cdb7e38632720ddeb47d7a1bc97ca307926b315373fcc77819d6aa46d33 |
| Notes | This work was supported by the YUTP‐FRG (015LC0‐442). Funding |
| ORCID | 0009-0005-7832-7235 |
| OpenAccessLink | https://onlinelibrary.wiley.com/doi/abs/10.1002%2Feng2.70358 |
| PageCount | 17 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_ce842a66c11744049ad6331118fec323 crossref_primary_10_1002_eng2_70358 wiley_primary_10_1002_eng2_70358_ENG270358 |
| PublicationCentury | 2000 |
| PublicationDate | September 2025 2025-09-00 2025-09-01 |
| PublicationDateYYYYMMDD | 2025-09-01 |
| PublicationDate_xml | – month: 09 year: 2025 text: September 2025 |
| PublicationDecade | 2020 |
| PublicationPlace | Hoboken, USA |
| PublicationPlace_xml | – name: Hoboken, USA |
| PublicationTitle | Engineering reports (Hoboken, N.J.) |
| PublicationYear | 2025 |
| Publisher | John Wiley & Sons, Inc Wiley |
| Publisher_xml | – name: John Wiley & Sons, Inc – name: Wiley |
| References | 2019; 7 2023; 56 2025; 7 2021; 202 2024; 96 2024; 32 2025; 15 2022; 20 2024; 11 2025 2024 2024; 14 2006; 313 2025; 10 2021; 13 2016; 6 2023 2021 2020 2024; 9 2022; 34 2019; 49 2025; 48 2024; 179 2022; 2 2025; 60 2025; 129 e_1_2_10_23_1 e_1_2_10_24_1 e_1_2_10_21_1 e_1_2_10_20_1 Khan I. K. (e_1_2_10_33_1) 2024 e_1_2_10_2_1 e_1_2_10_4_1 e_1_2_10_3_1 e_1_2_10_19_1 e_1_2_10_6_1 e_1_2_10_16_1 e_1_2_10_5_1 e_1_2_10_17_1 e_1_2_10_38_1 e_1_2_10_8_1 e_1_2_10_14_1 e_1_2_10_37_1 e_1_2_10_7_1 e_1_2_10_15_1 e_1_2_10_36_1 e_1_2_10_12_1 e_1_2_10_35_1 e_1_2_10_9_1 e_1_2_10_13_1 e_1_2_10_34_1 e_1_2_10_10_1 e_1_2_10_11_1 e_1_2_10_32_1 e_1_2_10_31_1 e_1_2_10_30_1 Alketbi S. (e_1_2_10_18_1) 2024 Genc S. (e_1_2_10_22_1) 2024 e_1_2_10_29_1 e_1_2_10_27_1 e_1_2_10_28_1 e_1_2_10_25_1 e_1_2_10_26_1 |
| References_xml | – volume: 14 year: 2024 article-title: The Impact of Diabetes Mellitus on the Development of Psychiatric and Neurological Disorders publication-title: Brain Disorders – volume: 20 start-page: 1 issue: 247 year: 2022 end-page: 12 article-title: The Need for Screening, Early Diagnosis, and Prediction of Chronic Kidney Disease in People With Diabetes in Low‐ and Middle‐Income Countries—A Review of the Current Literature publication-title: BMC Medicine – volume: 96 year: 2024 article-title: Optimizing Diabetic Retinopathy Detection With Inception‐V4 and Dynamic Version of Snow Leopard Optimization Algorithm publication-title: Biomedical Signal Processing and Control – start-page: 1409 year: 2020 end-page: 1413 – volume: 202 year: 2021 article-title: Diabetes Detection Using Deep Learning Techniques With Oversampling and Feature Augmentation publication-title: Computer Methods and Programs in Biomedicine – volume: 49 start-page: 1723 issue: 3 year: 2019 end-page: 1735 article-title: Discriminative Autoencoder for Feature Extraction: Application to Character Recognition publication-title: Neural Processing Letters – volume: 129 start-page: 60 issue: 1 year: 2025 end-page: 74 article-title: Regularized Stacked Autoencoder With Dropout‐Layer to Overcome Overfitting in Numerical High‐Dimensional Sparse Data publication-title: Journal of Advanced Research Design – year: 2024 – volume: 179 year: 2024 article-title: Current Strategies to Address Data Scarcity in Artificial Intelligence‐Based Drug Discovery : A Comprehensive Review publication-title: Computers in Biology and Medicine – volume: 6 start-page: 1 year: 2016 end-page: 10 article-title: Deep Patient: An Unsupervised Representation to Predict the Future of Patients From the Electronic Health Records publication-title: Scientific Reports – volume: 56 start-page: 13521 issue: 11 year: 2023 end-page: 13617 article-title: Deep Learning Modelling Techniques: Current Progress, Applications, Advantages, and Challenges publication-title: Artificial Intelligence Review – volume: 14 issue: 5 year: 2024 article-title: Cardiovascular Health Management in Diabetic Patients With Machine‐Learning‐Driven Predictions and Interventions publication-title: Applied Sciences – volume: 313 start-page: 504 issue: 5786 year: 2006 end-page: 507 article-title: Reducing the Dimensionality of Data With Neural Networks publication-title: Science – start-page: 1 year: 2024 end-page: 5 – volume: 13 year: 2021 article-title: Machine Learning and Deep Learning Predictive Models for Type 2 Diabetes: A Systematic Review publication-title: Diabetology & Metabolic Syndrome – start-page: 1 year: 2024 end-page: 7 – start-page: 324 year: 2024 end-page: 329 – volume: 60 start-page: 126 issue: 2 year: 2025 end-page: 140 article-title: The Role of Artificial Intelligence for Early Diagnostic Tools of Autism Spectrum Disorder: A Systematic Review publication-title: Turkish Archives of Pediatrics – volume: 32 start-page: 216 issue: 1 year: 2024 end-page: 222 article-title: Predictions of Diabetes Through Machine Learning Models Based on the Health Indicators Dataset publication-title: Applied and Computational Engineering – volume: 2 year: 2022 article-title: An Assessment of Machine Learning Models and Algorithms for Early Prediction and Diagnosis of Diabetes Using Health Indicators publication-title: Healthcare Analytics – volume: 15 year: 2025 article-title: Algorithmic and Mathematical Modeling for Synthetically Controlled Overlapping publication-title: Scientific Reports – volume: 48 start-page: S27 year: 2025 end-page: S49 article-title: 2. Diagnosis and Classification of Diabetes: Standards of Care in Diabetes—2025 publication-title: Diabetes Care – volume: 11 start-page: 58 year: 2024 end-page: 109 article-title: Machine Learning and Deep Learning‐Based Approach in Smart Healthcare: Recent Advances , Applications , Challenges and Opportunities publication-title: AIMS Public Health – volume: 10 start-page: 1 year: 2025 end-page: 11 article-title: IoT‐Driven Skin Cancer Detection: Active Learning and Hyperparameter Optimization for Enhanced Accuracy publication-title: IEEE Journal of Biomedical and Health Informatics – volume: 7 start-page: 530 issue: 4 year: 2019 end-page: 535 article-title: Type 2 Diabetes Data Classification Using Stacked Autoencoders in Deep Neural Networks publication-title: Clinical Epidemiology and Global Health – year: 2025 – volume: 9 start-page: 25070 issue: 9 year: 2024 end-page: 25097 article-title: Addressing Limitations of the K‐Means Clustering Algorithm: Outliers, Non‐Spherical Data, and Optimal Cluster Selection publication-title: AIMS Math – year: 2023 – volume: 7 year: 2025 article-title: Machine Learning and Artificial Intelligence in Type 2 Diabetes Prediction: A Comprehensive 33‐Year Bibliometric and Literature Analysis publication-title: Frontiers in Digital Health – year: 2021 article-title: Edinburgh Research Explorer IDF Diabetes Atlas publication-title: Glob. Reg. country‐level diabetes Preval. Estim. 2021 Proj 2045 – volume: 34 start-page: 1319 issue: 2 year: 2022 end-page: 1327 article-title: Deep Convolutional Neural Network for Diabetes Mellitus Prediction publication-title: Neural Computing & Applications – start-page: 120 year: 2023 end-page: 125 – start-page: 1 year: 2024 end-page: 6 – volume: 13 start-page: 146 issue: 1 year: 2021 end-page: 149 article-title: Classification of Diabetes Using Deep Learning and SVM Techniques publication-title: International Journal of Current Research and Review – ident: e_1_2_10_25_1 doi: 10.1126/science.1127647 – start-page: 1 volume-title: Advances in Science and Engineering Technology International Conferences (ASET) year: 2024 ident: e_1_2_10_18_1 – ident: e_1_2_10_21_1 doi: 10.54254/2755‐2721/32/20230214 – ident: e_1_2_10_12_1 doi: 10.1007/s10462‐023‐10466‐8 – ident: e_1_2_10_11_1 doi: 10.1016/j.bspc.2024.106501 – ident: e_1_2_10_26_1 doi: 10.1109/ICASSP40776.2020.9054412 – ident: e_1_2_10_27_1 doi: 10.1016/j.cegh.2018.12.004 – ident: e_1_2_10_29_1 doi: 10.1016/j.cmpb.2021.105968 – ident: e_1_2_10_30_1 doi: 10.1038/srep26094 – ident: e_1_2_10_5_1 doi: 10.1186/s13098‐021‐00767‐9 – ident: e_1_2_10_31_1 doi: 10.1109/ICAIT61638.2024.10690358 – ident: e_1_2_10_36_1 – ident: e_1_2_10_8_1 doi: 10.5152/TurkArchPediatr.2025.24183 – start-page: 1 volume-title: 8th International Artificial Intelligence and Data Processing Symposium (IDAP) year: 2024 ident: e_1_2_10_22_1 – ident: e_1_2_10_35_1 doi: 10.1038/s41598‐025‐87992‐8 – ident: e_1_2_10_2_1 doi: 10.1016/j.dscb.2024.100135 – ident: e_1_2_10_6_1 doi: 10.3389/fdgth.2025.1557467 – ident: e_1_2_10_10_1 doi: 10.1016/j.compbiomed.2024.108734 – ident: e_1_2_10_13_1 doi: 10.1007/s10462-023-10662-6 – ident: e_1_2_10_16_1 doi: 10.58445/rars.546 – ident: e_1_2_10_34_1 doi: 10.3934/math.20241222 – ident: e_1_2_10_9_1 doi: 10.3934/publichealth.2024004 – ident: e_1_2_10_7_1 doi: 10.1186/s12916‐022‐02438‐6 – ident: e_1_2_10_14_1 – ident: e_1_2_10_15_1 doi: 10.1109/JBHI.2025.3578419 – ident: e_1_2_10_4_1 doi: 10.2337/dc25-S002 – start-page: 020011 volume-title: AIP Conference Proceedings year: 2024 ident: e_1_2_10_33_1 – ident: e_1_2_10_32_1 doi: 10.31782/IJCRR.2021.13127 – ident: e_1_2_10_17_1 doi: 10.1145/3615366.3615376 – ident: e_1_2_10_19_1 doi: 10.1109/ICICoS62600.2024.10636871 – ident: e_1_2_10_38_1 doi: 10.37934/ard.129.1.6074 – ident: e_1_2_10_24_1 doi: 10.1016/j.health.2022.100118 – ident: e_1_2_10_37_1 doi: 10.1007/s11063‐018‐9894‐5 – ident: e_1_2_10_3_1 doi: 10.1016/j.diabres.2021.109119 – ident: e_1_2_10_23_1 doi: 10.1109/ICCCNT61001.2024.10724390 – ident: e_1_2_10_20_1 doi: 10.3390/app14052132 – ident: e_1_2_10_28_1 doi: 10.1007/s00521‐021‐06431‐7 |
| SSID | ssj0002171030 |
| Score | 2.3087015 |
| Snippet | ABSTRACT
Early and precise prediction of Type 2 diabetes is vital for effective intervention. However, extracting meaningful insights from high‐dimensional... Early and precise prediction of Type 2 diabetes is vital for effective intervention. However, extracting meaningful insights from high‐dimensional datasets... ABSTRACT Early and precise prediction of Type 2 diabetes is vital for effective intervention. However, extracting meaningful insights from high‐dimensional... |
| SourceID | doaj crossref wiley |
| SourceType | Open Website Index Database Publisher |
| SubjectTerms | autoencoder hybrid model machine learning models sparse data Type 2 diabetes prediction |
| SummonAdditionalLinks | – databaseName: Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LS8NAEF5EPOhBfGJ9saAnIbbZTXeTYyutPdTSg0pvYV8pgqQlbRVv_gR_o7_EmU0s9aIXTwkhsGG-nZlv2Mk3hFyapoojl4SBzmwjiHQILqUiHggdC9tsMid8V-VjXw4G8WiUDFdGfWFPWCkPXBqublwcMSWECUOvZZcoKzgHD40zZzjzOp8NmawUUxiDgWjj_KylHimru3zMrmF742z3lQzkhfp_ElOfWbo7ZLuihLRVfsouWXP5HtlaEQrcJ699oIT5nCJhWxTu8_2jDdnHUqwiKaNVW8uMDgs8d0FbU98LQBXtveE_WRRIJfgrXKdQyTraWswnKGFpXUFVbumd76l0tJJbHVOckfY8OyAP3c79TS-oRiYEhnMRB8A-VdY0qJnMY8HxkBXil46klSrUJpFGgVMnTGgOoU7yzBgpgRRYoVQkLOeHZD2f5O6IUGlFw3KgE5lmkYo0wMi4BgRUphi4fY1cfJsxnZbKGGmpgcxSNHbqjV0jbbTw8g1Us_YPAOO0wjj9C-MaufL4_LJO2hncMn93_B8rnpBNhoN-fTPZKVmfFwt3RjbMy_xpVpz7vfYFGonW3g priority: 102 providerName: Directory of Open Access Journals |
| Title | Latent Feature‐Based Type 2 Diabetes Prediction Using a Hybrid Stacked Sparse Autoencoder and Machine Learning Models |
| URI | https://onlinelibrary.wiley.com/doi/abs/10.1002%2Feng2.70358 https://doaj.org/article/ce842a66c11744049ad6331118fec323 |
| Volume | 7 |
| WOSCitedRecordID | wos001585376600021&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2577-8196 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002171030 issn: 2577-8196 databaseCode: DOA dateStart: 20190101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2577-8196 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002171030 issn: 2577-8196 databaseCode: M~E dateStart: 20190101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Engineering Database customDbUrl: eissn: 2577-8196 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002171030 issn: 2577-8196 databaseCode: M7S dateStart: 20191201 isFulltext: true titleUrlDefault: http://search.proquest.com providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2577-8196 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002171030 issn: 2577-8196 databaseCode: BENPR dateStart: 20191201 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2577-8196 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002171030 issn: 2577-8196 databaseCode: PIMPY dateStart: 20191201 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest – providerCode: PRVWIB databaseName: Wiley Online Library Free Content customDbUrl: eissn: 2577-8196 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002171030 issn: 2577-8196 databaseCode: WIN dateStart: 20190101 isFulltext: true titleUrlDefault: https://onlinelibrary.wiley.com providerName: Wiley-Blackwell – providerCode: PRVWIB databaseName: Wiley Online Library Open Access customDbUrl: eissn: 2577-8196 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002171030 issn: 2577-8196 databaseCode: 24P dateStart: 20190101 isFulltext: true titleUrlDefault: https://authorservices.wiley.com/open-science/open-access/browse-journals.html providerName: Wiley-Blackwell |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1La9wwEB7STQ_NoUlfZNMkCNpTwc1akiUbetmUzQPSZQ995Gb08lII3uDdTektP6G_Mb8kM1pn070EQi62MTIW89InafQNwEeXmVyGIk1s5XuJtCm6lJEiUTZXPst4UDGr8ueZHg7z8_NitAZf7s7CLPghlgtu5BkxXpODGzs9uCcNDfWYf0Z7zfJnsJ6mQpNNczlarrAg2KYaWlRdLtMYitHWlvyk_OD-85URKRL3rwLVONIcbT6tj1vwskWYrL8wiVewFurXsPEf7-Ab-HOGCLOeMcJ_8ybcXP87xMHMM5qUMs7aLJkpGzW0jUOqYzG1gBl28peOeDHEqOj-eL_EiXFg_flsQoyYPjTM1J59iymagbXsrWNGJdcupm_hx9Hg-9eTpK3AkDghVI4y86bKHFEwi1wJ2rPFcGil9tqk1hXaGYwRBVdWYOTUonJOaxS0V8ZI5YV4B516UodtYNqrnheITirLpZEWrYILK3vSVIZjFOnChzstlJcLoo1yQanMSxJjGcXYhUNS0LIFkWPHF5NmXLa-VrqQS26Ucmka6Q8L45UQGNTzKjjBRRc-RaU98J9yMDzm8WnnMY3fwwtO9YFjDtoudGbNPOzBc3c1-z1t9qN57sdZP15_nQ5vAc0f5_I |
| linkProvider | Wiley-Blackwell |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LT9wwEB61tFLh0AcFsdCHpXJCCmxsx06OUEG36rLaA0XcIr-yQkJZlN0FceMn8Bv5Jcx406VckKqeEkWOEs3jm7E9_gZg22Uml6FIE1v5biJtii5lpEiUzZXPMh5UrKo87evBID87K4ZtbQ6dhZnzQywW3MgzIl6Tg9OC9N4ja2ioR3wXDTbLX8IriWGJKvq4HC6WWDDbpiZa1F4u04jFaGwLglK-9_j6k5AUmfufZqox1By9-8-ffA9v2xyT7c-N4gO8CPUqrPzFPPgRrvuYY9ZTRhngrAn3t3cHGM48o2kp46ytk5mwYUMbOaQ8FosLmGG9GzrkxTBLRQDA6yVOjQPbn03HxInpQ8NM7dlxLNIMrOVvHTFqunYxWYPfR4cn33tJ24MhcUKoHIXmTZU5ImEWuRK0a4uAaKX22qTWFdoZRImCKysQO7WonNMaJe2VMVJ5IdZhqR7XYQOY9qrrBeYnleXSSIt2wYWVXWkqwxFHOvDtjxrKyznVRjknVeYlibGMYuzAAWloMYLoseODcTMqW28rXcglN0q5NI0EiIXxSgiE9bwKTnDRgZ2otWe-Ux4OfvB4t_kvg7_Cm97Jcb_s_xz82oJlTt2CY0XaJ1iaNrPwGV67q-n5pPkSbfUBqw3p0g |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LT9tAEB4VWqH2QB9QEaDtSu2pkku8u961j7xSKkKUQ1txs_blqFLlRE5CxY2fwG_klzCzcUO5IKGebFlr2ZrHN7O7s98AfHKZyWUo0sRWvptIm6JLGSkSZXPls4wHFasqf_b1YJCfnxfDtjaHzsIs-CGWC27kGRGvycHDxFd7d6yhoR7xL2iwWb4CT2Wmo19yOVwusWC2TU20qL1cphGL0diWBKV87-71eyEpMvffz1RjqOm9_M-ffAXrbY7J9hdG8RqehPoNvPiHeXAD_vQxx6xnjDLAeRNurq4PMJx5RtNSxllbJzNlw4Y2ckh5LBYXMMNOLumQF8MsFQEArxOcGge2P5-NiRPTh4aZ2rOzWKQZWMvfOmLUdO33dBN-9I6_H54kbQ-GxAmhchSaN1XmiIRZ5ErQri0CopXaa5NaV2hnECUKrqxA7NSick5rlLRXxkjlhXgLq_W4DlvAtFddLzA_qSyXRlq0Cy6s7EpTGY440oGPf9VQThZUG-WCVJmXJMYyirEDB6Sh5Qiix44Pxs2obL2tdCGX3Cjl0jQSIBbGKyEQ1vMqOMFFBz5HrT3wnfJ48JXHu-3HDP4Aa8OjXtn_NjjdgeecmgXHgrRdWJ018_AOnrmL2a9p8z6a6i1VhOlN |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Latent+Feature%E2%80%90Based+Type+2+Diabetes+Prediction+Using+a+Hybrid+Stacked+Sparse+Autoencoder+and+Machine+Learning+Models&rft.jtitle=Engineering+reports+%28Hoboken%2C+N.J.%29&rft.au=Abdussamad&rft.au=Daud%2C+Hanita&rft.au=Sokkalingam%2C+Rajalingam&rft.au=Zubair%2C+Muhammad&rft.date=2025-09-01&rft.issn=2577-8196&rft.eissn=2577-8196&rft.volume=7&rft.issue=9&rft_id=info:doi/10.1002%2Feng2.70358&rft.externalDBID=n%2Fa&rft.externalDocID=10_1002_eng2_70358 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2577-8196&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2577-8196&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2577-8196&client=summon |