Fine-Tuning Fuzzy KNN Classifier Based on Uncertainty Membership for the Medical Diagnosis of Diabetes

Diabetes, a metabolic disease in which the blood glucose level rises over time, is one of the most common chronic diseases at present. It is critical to accurately predict and classify diabetes to reduce the severity of the disease and treat it early. One of the difficulties that researchers face is...

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Vydané v:	Applied sciences Ročník 12; číslo 3; s. 950
Hlavní autori:	Salem, Hanaa, Shams, Mahmoud Y., Elzeki, Omar M., Abd Elfattah, Mohamed, F. Al-Amri, Jehad, Elnazer, Shaima
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Basel MDPI AG 01.02.2022
Predmet:	Algorithms Artificial intelligence Big Data Carbohydrates Classification classifier Deep learning diabetes Disease ensemble classifier fuzzy KNN Fuzzy logic Gestational diabetes Glucose Hormones Insulin Machine learning Medical diagnosis Metabolism Ontology Overweight Pima Indians diabetes dataset Pregnancy Womens health
ISSN:	2076-3417, 2076-3417
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Abstract	Diabetes, a metabolic disease in which the blood glucose level rises over time, is one of the most common chronic diseases at present. It is critical to accurately predict and classify diabetes to reduce the severity of the disease and treat it early. One of the difficulties that researchers face is that diabetes datasets are limited and contain outliers and missing data. Additionally, there is a trade-off between classification accuracy and operational law for detecting diabetes. In this paper, an algorithm for diabetes classification is proposed for pregnant women using the Pima Indians Diabetes Dataset (PIDD). First, a preprocessing step in the proposed algorithm includes outlier rejection, imputing missing values, the standardization process, and feature selection of the attributes, which enhance the dataset’s quality. Second, the classifier uses the fuzzy KNN method and modifies the membership function based on the uncertainty theory. Third, a grid search method is applied to achieve the best values for tuning the fuzzy KNN method based on uncertainty membership, as there are hyperparameters that affect the performance of the proposed classifier. In turn, the proposed tuned fuzzy KNN based on uncertainty classifiers (TFKNN) deals with the belief degree, handles membership functions and operation law, and avoids making the wrong categorization. The proposed algorithm performs better than other classifiers that have been trained and evaluated, including KNN, fuzzy KNN, naïve Bayes (NB), and decision tree (DT). The results of different classifiers in an ensemble could significantly improve classification precision. The TFKNN has time complexity O(kn2d), and space complexity O(n2d). The TFKNN model has high performance and outperformed the others in all tests in terms of accuracy, specificity, precision, and average AUC, with values of 90.63, 85.00, 93.18, and 94.13, respectively. Additionally, results of empirical analysis of TFKNN compared to fuzzy KNN, KNN, NB, and DT demonstrate the global superiority of TFKNN in precision, accuracy, and specificity.
AbstractList	Diabetes, a metabolic disease in which the blood glucose level rises over time, is one of the most common chronic diseases at present. It is critical to accurately predict and classify diabetes to reduce the severity of the disease and treat it early. One of the difficulties that researchers face is that diabetes datasets are limited and contain outliers and missing data. Additionally, there is a trade-off between classification accuracy and operational law for detecting diabetes. In this paper, an algorithm for diabetes classification is proposed for pregnant women using the Pima Indians Diabetes Dataset (PIDD). First, a preprocessing step in the proposed algorithm includes outlier rejection, imputing missing values, the standardization process, and feature selection of the attributes, which enhance the dataset’s quality. Second, the classifier uses the fuzzy KNN method and modifies the membership function based on the uncertainty theory. Third, a grid search method is applied to achieve the best values for tuning the fuzzy KNN method based on uncertainty membership, as there are hyperparameters that affect the performance of the proposed classifier. In turn, the proposed tuned fuzzy KNN based on uncertainty classifiers (TFKNN) deals with the belief degree, handles membership functions and operation law, and avoids making the wrong categorization. The proposed algorithm performs better than other classifiers that have been trained and evaluated, including KNN, fuzzy KNN, naïve Bayes (NB), and decision tree (DT). The results of different classifiers in an ensemble could significantly improve classification precision. The TFKNN has time complexity O(kn2d), and space complexity O(n2d). The TFKNN model has high performance and outperformed the others in all tests in terms of accuracy, specificity, precision, and average AUC, with values of 90.63, 85.00, 93.18, and 94.13, respectively. Additionally, results of empirical analysis of TFKNN compared to fuzzy KNN, KNN, NB, and DT demonstrate the global superiority of TFKNN in precision, accuracy, and specificity.
Author	Elzeki, Omar M. Abd Elfattah, Mohamed Salem, Hanaa F. Al-Amri, Jehad Shams, Mahmoud Y. Elnazer, Shaima
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Cites_doi	10.2337/diacare.27.2007.S88 10.1016/j.asoc.2021.107423 10.21500/20112084.844 10.1016/j.jnca.2020.102873 10.1109/ICNN.1996.549118 10.1109/ITME.2018.00021 10.1109/FOCS.2006.49 10.4239/wjd.v5.i4.444 10.1016/j.eswa.2008.09.015 10.1016/j.artmed.2020.101815 10.1109/TSMC.1985.6313426 10.1007/s10916-011-9762-6 10.1016/j.diabres.2018.02.023 10.1007/s40200-020-00520-5 10.1016/j.cmpb.2021.105968 10.1109/TPAMI.2011.142 10.1016/j.ceh.2020.11.001 10.1049/htl2.12010 10.1016/j.csda.2010.11.018 10.1088/1742-6596/978/1/012047 10.1109/SSCI47803.2020.9308286 10.1007/978-3-030-66717-7_13 10.1016/j.csbj.2016.12.005 10.1016/j.artmed.2015.08.003 10.1016/j.ins.2011.09.027 10.1109/ICCISci.2019.8716405 10.1016/j.patcog.2010.01.003 10.1016/j.ins.2014.08.056 10.1016/j.atherosclerosissup.2014.04.001 10.1016/j.ins.2013.10.038 10.1145/3127404.3127466 10.1136/bmjdrc-2017-000438 10.1016/0167-8655(83)90064-8 10.1016/j.biopha.2019.108802 10.1016/j.procs.2018.05.122 10.1515/phys-2020-0159 10.2337/diacare.23.9.1278 10.1016/j.jbi.2018.08.006 10.1016/j.cmpb.2018.01.004 10.1109/ACCESS.2020.2989857 10.1088/1742-6596/1821/1/012006 10.5121/ijsc.2011.2202 10.1080/03772063.2018.1462109 10.1038/s41572-019-0098-8 10.1007/978-3-030-24409-5_18
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References	Garcia (ref_42) 2012; 34 ref_56 Ramsingh (ref_18) 2021; 108 Nnamoko (ref_50) 2020; 104 Venkatesh (ref_52) 2019; 19 Thakkar (ref_8) 2021; 4 Bhoi (ref_26) 2021; 12 Ayon (ref_36) 2019; 11 ref_17 ref_16 Maulidina (ref_30) 2021; 1821 Naz (ref_34) 2020; 19 Khanwalkar (ref_25) 2020; 8 Mokdad (ref_2) 2000; 23 Kavakiotis (ref_19) 2017; 15 Albahri (ref_10) 2021; 173 Airola (ref_57) 2011; 55 Ramesh (ref_29) 2021; 8 Sisodia (ref_23) 2018; 132 Keller (ref_49) 1985; SMC-15 ref_21 (ref_4) 2014; 5 Benavides (ref_33) 2021; 202 Sabir (ref_1) 2019; 114 Syaliman (ref_43) 2018; 978 ref_28 ref_27 Hsieh (ref_54) 2012; 36 Crandall (ref_12) 2017; 5 Srivastava (ref_35) 2014; 15 Versaci (ref_7) 2020; 18 Luukka (ref_6) 2009; 36 Boulos (ref_9) 2021; 20 Harangi (ref_55) 2018; 86 ref_31 Elmogy (ref_22) 2015; 65 McIntyre (ref_13) 2019; 5 Cho (ref_15) 2018; 138 ref_39 ref_38 Sattar (ref_11) 2014; 15 ref_37 Kuhkan (ref_44) 2016; 8 Cousineau (ref_51) 2010; 3 Association (ref_14) 2004; 27 ref_45 Hasan (ref_53) 2020; 8 Samant (ref_20) 2018; 157 Zhu (ref_32) 2015; 292 Liaw (ref_40) 2010; 43 ref_3 Derrac (ref_41) 2012; 186 Patel (ref_46) 2018; 65 Karegowda (ref_24) 2011; 2 ref_5 Derrac (ref_47) 2014; 260 (ref_48) 1983; 1
References_xml	– volume: 27 start-page: S88 year: 2004 ident: ref_14 article-title: Gestational diabetes mellitus publication-title: Diabetes Care doi: 10.2337/diacare.27.2007.S88 – volume: 108 start-page: 107423 year: 2021 ident: ref_18 article-title: An integrated multi-node Hadoop framework to predict high-risk factors of Diabetes Mellitus using a Multilevel MapReduce based Fuzzy Classifier (MMR-FC) and Modified DBSCAN algorithm publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2021.107423 – volume: 3 start-page: 58 year: 2010 ident: ref_51 article-title: Outliers detection and treatment: A review publication-title: Int. J. Psychol. Res. doi: 10.21500/20112084.844 – ident: ref_5 – volume: 173 start-page: 102873 year: 2021 ident: ref_10 article-title: IoT-based telemedicine for disease prevention and health promotion: State-of-the-Art publication-title: J. Netw. Comput. Appl. doi: 10.1016/j.jnca.2020.102873 – volume: 8 start-page: 973 year: 2020 ident: ref_25 article-title: Sequential Minimal Optimization for Predicting Diabetes at its Early Stage publication-title: J. Crit. Rev. – ident: ref_38 doi: 10.1109/ICNN.1996.549118 – ident: ref_3 doi: 10.1109/ITME.2018.00021 – ident: ref_39 doi: 10.1109/FOCS.2006.49 – volume: 20 start-page: 1 year: 2021 ident: ref_9 article-title: Smart city lifestyle sensing, big data, geo-analytics and intelligence for smarter public health decision-making in overweight, obesity and type 2 diabetes prevention: The research we should be doing publication-title: Int. J. Health Geogr. – volume: 5 start-page: 444 year: 2014 ident: ref_4 article-title: Type 2 diabetes and cardiovascular disease: Have all risk factors the same strength? publication-title: World J. Diabetes doi: 10.4239/wjd.v5.i4.444 – ident: ref_16 – volume: 8 start-page: 90 year: 2016 ident: ref_44 article-title: A method to improve the accuracy of k-nearest neighbor algorithm publication-title: Int. J. Comput. Eng. Inf. Technol. – volume: 36 start-page: 7463 year: 2009 ident: ref_6 article-title: Classification based on fuzzy robust PCA algorithms and similarity classifier publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2008.09.015 – volume: 12 start-page: 3074 year: 2021 ident: ref_26 article-title: Prediction of Diabetes in Females of Pima Indian Heritage: A Complete Supervised Learning Approach publication-title: Turk. J. Comput. Math. Educ. TURCOMAT – volume: 104 start-page: 101815 year: 2020 ident: ref_50 article-title: Efficient treatment of outliers and class imbalance for diabetes prediction publication-title: Artif. Intell. Med. doi: 10.1016/j.artmed.2020.101815 – volume: SMC-15 start-page: 580 year: 1985 ident: ref_49 article-title: A fuzzy K-nearest neighbor algorithm publication-title: IEEE Trans. Syst. Man Cybern. doi: 10.1109/TSMC.1985.6313426 – volume: 36 start-page: 2841 year: 2012 ident: ref_54 article-title: Design ensemble machine learning model for breast cancer diagnosis publication-title: J. Med. Syst. doi: 10.1007/s10916-011-9762-6 – volume: 19 start-page: 3 year: 2019 ident: ref_52 article-title: A Review of Feature Selection and Its Methods publication-title: Cybern. Inf. Technol. – volume: 138 start-page: 271 year: 2018 ident: ref_15 article-title: IDF Diabetes Atlas: Global estimates of diabetes prevalence for 2017 and projections for 2045 publication-title: Diabetes Res. Clin. Pract. doi: 10.1016/j.diabres.2018.02.023 – volume: 19 start-page: 391 year: 2020 ident: ref_34 article-title: Deep learning approach for diabetes prediction using PIMA Indian dataset publication-title: J. Diabetes Metab. Disord. doi: 10.1007/s40200-020-00520-5 – ident: ref_17 – volume: 202 start-page: 105968 year: 2021 ident: ref_33 article-title: Diabetes detection using deep learning techniques with oversampling and feature augmentation publication-title: Comput. Methods Programs Biomed. doi: 10.1016/j.cmpb.2021.105968 – volume: 34 start-page: 417 year: 2012 ident: ref_42 article-title: Prototype Selection for Nearest Neighbor Classification: Taxonomy and Empirical Study publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2011.142 – volume: 11 start-page: 21 year: 2019 ident: ref_36 article-title: Diabetes Prediction: A Deep Learning Approach publication-title: Int. J. Inf. Eng. Electron. Bus. – volume: 4 start-page: 12 year: 2021 ident: ref_8 article-title: Comparative anatomization of data mining and fuzzy logic techniques used in diabetes prognosis publication-title: Clin. eHealth doi: 10.1016/j.ceh.2020.11.001 – volume: 8 start-page: 45 year: 2021 ident: ref_29 article-title: A remote healthcare monitoring framework for diabetes prediction using machine learning publication-title: Healthc. Technol. Lett. doi: 10.1049/htl2.12010 – volume: 55 start-page: 1828 year: 2011 ident: ref_57 article-title: An experimental comparison of cross-validation techniques for estimating the area under the ROC curve publication-title: Comput. Stat. Data Anal. doi: 10.1016/j.csda.2010.11.018 – volume: 978 start-page: 012047 year: 2018 ident: ref_43 article-title: Improving the accuracy of k-nearest neighbor using local mean based and distance weight publication-title: J. Phys. Conf. Ser. doi: 10.1088/1742-6596/978/1/012047 – ident: ref_45 doi: 10.1109/SSCI47803.2020.9308286 – ident: ref_31 doi: 10.1007/978-3-030-66717-7_13 – volume: 15 start-page: 104 year: 2017 ident: ref_19 article-title: Machine Learning and Data Mining Methods in Diabetes Research publication-title: Comput. Struct. Biotechnol. J. doi: 10.1016/j.csbj.2016.12.005 – volume: 65 start-page: 179 year: 2015 ident: ref_22 article-title: A fuzzy-ontology-oriented case-based reasoning framework for semantic diabetes diagnosis publication-title: Artif. Intell. Med. doi: 10.1016/j.artmed.2015.08.003 – volume: 186 start-page: 73 year: 2012 ident: ref_41 article-title: Enhancing evolutionary instance selection algorithms by means of fuzzy rough set based feature selection publication-title: Inf. Sci. doi: 10.1016/j.ins.2011.09.027 – ident: ref_27 doi: 10.1109/ICCISci.2019.8716405 – volume: 43 start-page: 2351 year: 2010 ident: ref_40 article-title: Fast exact k nearest neighbors search using an orthogonal search tree publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2010.01.003 – ident: ref_37 – volume: 292 start-page: 1 year: 2015 ident: ref_32 article-title: An improved early detection method of type-2 diabetes mellitus using multiple classifier system publication-title: Inf. Sci. doi: 10.1016/j.ins.2014.08.056 – volume: 15 start-page: 1 year: 2014 ident: ref_11 article-title: The use of statins in people at risk of developing diabetes mellitus: Evidence and guidance for clinical practice publication-title: Atheroscler. Suppl. doi: 10.1016/j.atherosclerosissup.2014.04.001 – ident: ref_21 – volume: 260 start-page: 98 year: 2014 ident: ref_47 article-title: Fuzzy nearest neighbor algorithms: Taxonomy, experimental analysis and prospects publication-title: Inf. Sci. doi: 10.1016/j.ins.2013.10.038 – ident: ref_28 doi: 10.1145/3127404.3127466 – volume: 5 start-page: e000438 year: 2017 ident: ref_12 article-title: Statin use and risk of developing diabetes: Results from the Diabetes Prevention Program publication-title: BMJ Open Diabetes Res. Care doi: 10.1136/bmjdrc-2017-000438 – volume: 15 start-page: 1929 year: 2014 ident: ref_35 article-title: Dropout: A simple way to prevent neural networks from overfitting publication-title: J. Mach. Learn. Res. – volume: 1 start-page: 287 year: 1983 ident: ref_48 article-title: A learning scheme for a fuzzy k-NN rule publication-title: Pattern Recognit. Lett. doi: 10.1016/0167-8655(83)90064-8 – volume: 114 start-page: 108802 year: 2019 ident: ref_1 article-title: Role of cadmium and arsenic as endocrine disruptors in the metabolism of carbohydrates: Inserting the association into perspectives publication-title: Biomed. Pharm. doi: 10.1016/j.biopha.2019.108802 – volume: 132 start-page: 1578 year: 2018 ident: ref_23 article-title: Prediction of diabetes using classification algorithms publication-title: Procedia Comput. Sci. doi: 10.1016/j.procs.2018.05.122 – volume: 18 start-page: 230 year: 2020 ident: ref_7 article-title: Joint use of eddy current imaging and fuzzy similarities to assess the integrity of steel plates publication-title: Open Phys. doi: 10.1515/phys-2020-0159 – volume: 23 start-page: 1278 year: 2000 ident: ref_2 article-title: Diabetes trends in the US: 1990–1998 publication-title: Diabetes Care doi: 10.2337/diacare.23.9.1278 – volume: 86 start-page: 25 year: 2018 ident: ref_55 article-title: Skin lesion classification with ensembles of deep convolutional neural networks publication-title: J. Biomed. Inform. doi: 10.1016/j.jbi.2018.08.006 – volume: 157 start-page: 121 year: 2018 ident: ref_20 article-title: Machine learning techniques for medical diagnosis of diabetes using iris images publication-title: Comput. Methods Programs Biomed. doi: 10.1016/j.cmpb.2018.01.004 – volume: 8 start-page: 76516 year: 2020 ident: ref_53 article-title: Diabetes Prediction Using Ensembling of Different Machine Learning Classifiers publication-title: IEEE Access doi: 10.1109/ACCESS.2020.2989857 – volume: 1821 start-page: 012006 year: 2021 ident: ref_30 article-title: Feature optimization using Backward Elimination and Support Vector Machines (SVM) algorithm for diabetes classification publication-title: J. Phys. Conf. Ser. doi: 10.1088/1742-6596/1821/1/012006 – volume: 2 start-page: 15 year: 2011 ident: ref_24 article-title: Application of Genetic Algorithm Optimized Neural Network Connection Weights for Medical Diagnosis of PIMA Indians Diabetes publication-title: Int. J. Soft Comput. doi: 10.5121/ijsc.2011.2202 – volume: 65 start-page: 780 year: 2018 ident: ref_46 article-title: An Improved Fuzzy K-Nearest Neighbor Algorithm for Imbalanced Data using Adaptive Approach publication-title: IETE J. Res. doi: 10.1080/03772063.2018.1462109 – volume: 5 start-page: 1 year: 2019 ident: ref_13 article-title: Gestational diabetes mellitus publication-title: Nat. Rev. Dis. Primer doi: 10.1038/s41572-019-0098-8 – ident: ref_56 doi: 10.1007/978-3-030-24409-5_18
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StartPage	950
SubjectTerms	Algorithms Artificial intelligence Big Data Carbohydrates Classification classifier Deep learning diabetes Disease ensemble classifier fuzzy KNN Fuzzy logic Gestational diabetes Glucose Hormones Insulin Machine learning Medical diagnosis Metabolism Ontology Overweight Pima Indians diabetes dataset Pregnancy Womens health
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Title	Fine-Tuning Fuzzy KNN Classifier Based on Uncertainty Membership for the Medical Diagnosis of Diabetes
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