7c39bef7-183d-4b56-ad14-5d95c5473ec9 beie director@blueeyesintelligence.org 10.35940/ijitee.F8748.0410621 10.1038/498255aV. Marx, "The big challenges of big data," Nature, vol. 498, no. 7453, pp. 255-260, 2013.10.1038/493473aC. A. Mattmann, "A vision for data science," Nature, vol. 493, no. 7433, pp. 473-475, 2013.10.1146/annurev.nutr.23.011702.073212L. S. Lieberman, "Dietary, evolutionary, and modernizing influences on the prevalence of type 2 diabetes," Annu. Rev. Nutr., vol. 23, no. 1, pp. 345-377, 2003.10.1111/j.1464-5491.1991.tb01540.xD. M. A. Jackson, R. Wills, J. Davies, K. Meadows, B. M. Singh, and P. H. Wise, "Public awareness of the symptoms of diabetes mellitus," Diabet. Med., vol. 8, no. 10, pp. 971-972, 1991.10.2337/diacare.15.7.815M. I. Harris, R. Klein, T. A. Welborn, and M. W. Knuiman, "Onset of NIDDM occurs at least 4-7 yr before clinical diagnosis," Diabetes Care, vol. 15, no. 7, pp. 815-819, 1992.10.1056/NEJMoa012512W. C. Knowler et al., "Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin.," N. Engl. J. Med., vol. 346, no. 6, pp. 393-403, 2002.B. Paulweber et al., "A European evidence-based guideline for the prevention of type 2 diabetes.," Horm. Metab. Res. Horm. Stoffwechselforschung= Horm. Metab., vol. 42, no. S 01, pp. S3-36, 2010.10.1055/s-0028-1087203P. E. H. Schwarz, J. Li, J. Lindstrom, and J. Tuomilehto, "Tools for predicting the risk of type 2 diabetes in daily practice," Horm. Metab. Res., vol. 41, no. 02, pp. 86-97, 2009.10.1146/annurev.bioeng.8.061505.095802P. Sajda, "Machine learning for detection and diagnosis of disease," Annu. Rev. Biomed. Eng., vol. 8, pp. 537-565, 2006.10.1186/s12911-019-0918-5A. Dinh, S. Miertschin, A. Young, and S. D. Mohanty, "A data-driven approach to predicting diabetes and cardiovascular disease with machine learning," BMC Med. Inform. Decis. Mak., vol. 19, no. 1, pp. 1-15, 2019, doi: 10.1186/s12911-019-0918-5.R. A. Wilson and F. C. Keil, The MIT encyclopedia of the cognitive sciences. MIT press, 2001.U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth, "From data mining to knowledge discovery in databases," AI Mag., vol. 17, no. 3, p. 37, 1996.S. Russell and P. Norvig, "Artificial intelligence: a modern approach," 2002.10.7551/mitpress/13811.001.0001E. Alpaydin, Introduction to machine learning. MIT press, 2020.10.2337/diacare.28.suppl_1.S37D. Mellitus, "Diagnosis and classification of diabetes mellitus," Diabetes Care, vol. 28, no. S37, pp. S5-S10, 2005.10.1177/193229681100500127E. J. Caveney and O. J. Cohen, "Diabetes and biomarkers," J. Diabetes Sci. Technol., vol. 5, no. 1, pp. 192-197, 2011.10.1016/j.compbiomed.2016.05.005H. F. Jelinek, A. Stranieri, A. Yatsko, and S. Venkatraman, "Data analytics identify glycated haemoglobin co-markers for type 2 diabetes mellitus diagnosis," Comput. Biol. Med., vol. 75, pp. 90-97, 2016.10.1016/j.cmpb.2017.09.004M. Maniruzzaman et al., "Comparative approaches for classification of diabetes mellitus data: Machine learning paradigm," Comput. Methods Programs Biomed., vol. 152, pp. 23-34, 2017, doi: 10.1016/j.cmpb.2017.09.004.10.1016/j.jclinepi.2015.10.002F. Bagherzadeh-Khiabani, A. Ramezankhani, F. Azizi, F. Hadaegh, E. W. Steyerberg, and D. Khalili, "A tutorial on variable selection for clinical prediction models: feature selection methods in data mining could improve the results," J. Clin. Epidemiol., vol. 71, pp. 76-85, 2016.10.1007/s11517-015-1263-1E. I. Georga, V. C. Protopappas, D. Polyzos, and D. I. Fotiadis, "Evaluation of short-term predictors of glucose concentration in type 1 diabetes combining feature ranking with regression models," Med. Biol. Eng. Comput., vol. 53, no. 12, pp. 1305-1318, 2015.10.1016/j.jbi.2015.02.001K.-J. Wang, A. M. Adrian, K.-H. Chen, and K.-M. Wang, "An improved electromagnetism-like mechanism algorithm and its application to the prediction of diabetes mellitus," J. Biomed. Inform., vol. 54, pp. 220-229, 2015.10.1016/j.eswa.2013.04.003M. W. Aslam, Z. Zhu, and A. K. Nandi, "Feature generation using genetic programming with comparative partner selection for diabetes classification," Expert Syst. Appl., vol. 40, no. 13, pp. 5402-5412, 2013.10.1016/j.compbiomed.2016.04.014C. Sideris, M. Pourhomayoun, H. Kalantarian, and M. Sarrafzadeh, "A flexible data-driven comorbidity feature extraction framework," Comput. Biol. Med., vol. 73, pp. 165-172, 2016.10.1177/0272989X14560647A. Ramezankhani, O. Pournik, J. Shahrabi, F. Azizi, F. Hadaegh, and D. Khalili, "The impact of oversampling with SMOTE on the performance of 3 classifiers in prediction of type 2 diabetes," Med. Decis. Mak., vol. 36, no. 1, pp. 137-144, 2016.10.1109/I-SMAC.2017.8058253G. D. Kalyankar, S. R. Poojara, and N. V. Dharwadkar, "Predictive analysis of diabetic patient data using machine learning and Hadoop," Proc. Int. Conf. IoT Soc. Mobile, Anal. Cloud, I-SMAC 2017, no. Dm, pp. 619-624, 2017, doi: 10.1109/I-SMAC.2017.8058253.10.1016/j.eswa.2011.01.017D. Çalişir and E. Doğantekin, "An automatic diabetes diagnosis system based on LDA-Wavelet Support Vector Machine Classifier," Expert Syst. Appl., vol. 38, no. 7, pp. 8311-8315, 2011.10.1016/j.eswa.2011.05.018M. F. Ganji and M. S. Abadeh, "A fuzzy classification system based on Ant Colony Optimization for diabetes disease diagnosis," Expert Syst. Appl., vol. 38, no. 12, pp. 14650-14659, 2011.10.1109/TITB.2012.2219876E. I. Georga et al., "Multivariate prediction of subcutaneous glucose concentration in type 1 diabetes patients based on support vector regression," IEEE J. Biomed. Heal. informatics, vol. 17, no. 1, pp. 71-81, 2012.10.1093/jamia/ocw028V. Agarwal et al., "Learning statistical models of phenotypes using noisy labeled training data," J. Am. Med. Informatics Assoc., vol. 23, no. 6, pp. 1166-1173, 2016.10.1016/j.artmed.2015.08.003S. El-Sappagh, M. Elmogy, and A. M. Riad, "A fuzzy-ontology-oriented case-based reasoning framework for semantic diabetes diagnosis," Artif. Intell. Med., vol. 65, no. 3, pp. 179-208, 2015.10.1007/s00146-013-0456-0A. Sarwar and V. Sharma, "Comparative analysis of machine learning techniques in prognosis of type II diabetes," AI Soc., vol. 29, no. 1, pp. 123-129, 2014, doi: 10.1007/s00146-013-0456-0.10.1109/TBME.2013.2282625B. Zhang, B. V. K. Vijaya Kumar, and D. Zhang, "Detecting diabetes mellitus and nonproliferative diabetic retinopathy using tongue color, texture, and geometry features," IEEE Trans. Biomed. Eng., vol. 61, no. 2, pp. 491-501, 2014, doi: 10.1109/TBME.2013.2282625.10.5120/ijca2017916020M. Aminul and N. Jahan, "Prediction of Onset Diabetes using Machine Learning Techniques," Int. J. Comput. Appl., vol. 180, no. 5, pp. 7-11, 2017, doi: 10.5120/ijca2017916020.10.1089/big.2015.0020N. Razavian, S. Blecker, A. M. Schmidt, A. Smith-McLallen, S. Nigam, and D. Sontag, "Population-level prediction of type 2 diabetes from claims data and analysis of risk factors," Big Data, vol. 3, no. 4, pp. 277-287, 2015.H. Núñez, C. Angulo, and A. Català, "Rule extraction from support vector machines.," in Esann, 2002, pp. 107-112.10.1016/j.jbi.2015.12.001S. Bashir, U. Qamar, and F. H. Khan, "IntelliHealth: a medical decision support application using a novel weighted multi-layer classifier ensemble framework," J. Biomed. Inform., vol. 59, pp. 185-200, 2016.10.1016/j.cmpb.2011.03.018A. Ozcift and A. Gulten, "Classifier ensemble construction with rotation forest to improve medical diagnosis performance of machine learning algorithms," Comput. Methods Programs Biomed., vol. 104, no. 3, pp. 443-451, 2011.10.1111/j.1468-0394.2010.00527.xE. D. Übeyli, "Automatic diagnosis of diabetes using adaptive neuro‐fuzzy inference systems," Expert Syst., vol. 27, no. 4, pp. 259-266, 2010.10.1007/978-3-642-13208-7_52M. Kordos, M. Blachnik, and D. Strzempa, "Do we need whatever more than k-NN?," in International Conference on Artificial Intelligence and Soft Computing, 2010, pp. 414-421.10.1109/TBME.2012.2188893C. Zecchin, A. Facchinetti, G. Sparacino, G. De Nicolao, and C. Cobelli, "Neural network incorporating meal information improves accuracy of short-time prediction of glucose concentration," IEEE Trans. Biomed. Eng., vol. 59, no. 6, pp. 1550-1560, 2012.10.1186/s12884-019-2374-8T. Zheng et al., "A simple model to predict risk of gestational diabetes mellitus from 8 to 20 weeks of gestation in Chinese women," BMC Pregnancy Childbirth, vol. 19, no. 1, pp. 1-10, 2019, doi: 10.1186/s12884-019-2374-8.10.1109/ICCMC.2019.8819841P. Sonar and K. Jaya Malini, "Diabetes prediction using different machine learning approaches," Proc. 3rd Int. Conf. Comput. Me'thodol. Commun. ICCMC 2019, no. Iccmc, pp. 367-371, 2019, doi: 10.1109/ICCMC.2019.8819841.10.1186/s40537-019-0175-6N. Sneha and T. Gangil, "Analysis of diabetes mellitus for early prediction using optimal features selection," J. Big Data, vol. 6, no. 1, 2019, doi: 10.1186/s40537-019-0175-6.10.1109/UBMYK48245.2019.8965542A. Al-Zebari and A. Sengur, "Performance Comparison of Machine Learning Techniques on Diabetes Disease Detection," 1st Int. Informatics Softw. Eng. Conf. Innov. Technol. Digit. Transform. IISEC 2019 - Proc., pp. 2-5, 2019, doi: 10.1109/UBMYK48245.2019.8965542.K. M. Varma and Dr. B.S. Panda, "Comparative analysis of Predicting Diabetes Using Machine Learning Techniques," J. Emerg. Technol. Innov. Res., vol. 6, no. 6, pp. 522-530, 2019, [Online]. Available: www.jetir.org.10.5888/pcd16.190109Z. Xie, O. Nikolayeva, J. Luo, and D. Li, "Building risk prediction models for type 2 diabetes using machine learning techniques," Prev. Chronic Dis., vol. 16, no. 9, pp. 1-9, 2019, doi: 10.5888/pcd16.190109.10.1016/j.procs.2020.01.047A. Mujumdar and V. Vaidehi, "Diabetes Prediction using Machine Learning Algorithms," Procedia Comput. Sci., vol. 165, pp. 292-299, 2019, doi: 10.1016/j.procs.2020.01.047.10.1210/clinem/dgaa899H. H. Wu YT, Zhang CJ, Mol BW, Kawai A, Li C, Chen L, Wang Y, Sheng JZ, Fan JX, Shi Y, "Early prediction of gestational diabetes mellitus in the Chinese population via advanced machine learning," J Clin Endocrinol Metab, no. 301, pp. 1-27, 2020, doi: 10.1210/clinem/dgaa899.10.1186/s12911-020-01318-4J. Ye, L. Yao, J. Shen, R. Janarthanam, and Y. Luo, "Predicting mortality in critically ill patients with diabetes using machine learning and clinical notes," BMC Med. Inform. Decis. Mak., vol. 20, no. 11, pp. 1-8, 2020, doi: 10.1186/s12911-020-01318-4.B. Pranto, S. M. Mehnaz, E. B. Mahid, I. M. Sadman, A. Rahman, and S. Momen, "Evaluating10.35940/ijitee.E2692.039520A. S. Hassan, I. Malaserene, and A. A. Leema, "Diabetes Mellitus Prediction using Classification Techniques," Int. J. Innov. Technol. Explor. Eng., vol. 9, no. 5, pp. 2080-2084, 2020, doi: 10.35940/ijitee.e2692.039520.S. Rani, "mining in Continuous data for Diabetes Prediction," 2018 Second Int. Conf. Intell. Comput. Control Syst., no. Iciccs, pp. 1209-1214, 2018.P. R. K. Varma, V. V. Kumari, and S. S. Kumar, Classification of Diabetes Mellitus Disease (DMD): A Data Mining (DM) Approach, vol. 710, no. Dmd. Springer Singapore, 2018.10.1145/3433996.3434025F. Hou, Z. X. Cheng, L. Y. Kang, and W. Zheng, "Prediction of Gestational Diabetes Based on LightGBM," ACM Int. Conf. Proceeding Ser., pp. 161-165, 2020, doi: 10.1145/3433996.3434025.10.1002/dmrr.3397H. Liu et al., "Machine learning risk score for prediction of gestational diabetes in early pregnancy in Tianjin, China," Diabetes. Metab. Res. Rev., no. February, 2020, doi: 10.1002/dmrr.3397.10.1155/2020/4168340Y. Ye, Y. Xiong, Q. Zhou, J. Wu, X. Li, and X. Xiao, "Comparison of Machine Learning Methods and Conventional Logistic Regressions for Predicting Gestational Diabetes Using Routine Clinical Data: A Retrospective Cohort Study," J. Diabetes Res., vol. 2020, 2020, [Online]. Available: https://www.hindawi.com/journals/jdr/2020/4168340/.10.2196/21573J. Shen et al., "An innovative artificial intelligence-based app for the diagnosis of gestational diabetes mellitus (GDM-AI): Development study," J. Med. Internet Res., vol. 22, no. 9, pp. 1-11, 2020, doi: 10.2196/21573.10.1038/s41598-020-61123-xL. Zhang, Y. Wang, M. Niu, C. Wang, and Z. Wang, "Machine learning for characterizing risk of type 2 diabetes mellitus in a rural Chinese population: the Henan Rural Cohort Study," Sci. Rep., vol. 10, no. 1, pp. 1-10, 2020, doi: 10.1038/s41598-020-61123-x.10.1109/ACCESS.2020.3042483E. A. Pustozerov et al., "Machine Learning Approach for Postprandial Blood Glucose Prediction in Gestational Diabetes Mellitus," IEEE Access, vol. 8, 2020, doi: 10.1109/ACCESS.2020.3042483.AUTHORS PROFILEKumar R, has Completed his B.E and M.E in Computer Science and Engineering in Anna University with First Class and Distinction. He is currently pursuing his research in Annamalai University, Chidambaram, Tamil Nadu., in the area of Data Mining. His research interest includes Data Mining, Pattern Classifications. He is also working as Assistant Professor in the Department of Information Science and Engineering in MVJ College of Engineering, Bangalore, he has more than 10 years of teaching experience in engineering college.Dr S Pazhanirajan has Completed his B.E and M.E in Computer Science and Engineering in Annamalai University He is currently working as Assistant Professor in the Department of Computer Science and Engineering, Annamalai University, Chidambaram, Tamil Nadu. His Research interest includes Data Mining, Pattern Classification, Audio and Image Processing. He has more than 14 years of Experience in Teaching and has more than 10 publications in reputed journals.10.1038/498255aV. Marx, "The big challenges of big data," Nature, vol. 498, no. 7453, pp. 255-260, 2013.10.1038/493473aC. A. Mattmann, "A vision for data science," Nature, vol. 493, no. 7433, pp. 473-475, 2013.10.1146/annurev.nutr.23.011702.073212L. S. Lieberman, "Dietary, evolutionary, and modernizing influences on the prevalence of type 2 diabetes," Annu. Rev. Nutr., vol. 23, no. 1, pp. 345-377, 2003.10.1111/j.1464-5491.1991.tb01540.xD. M. A. Jackson, R. Wills, J. Davies, K. Meadows, B. M. Singh, and P. H. Wise, "Public awareness of the symptoms of diabetes mellitus," Diabet. Med., vol. 8, no. 10, pp. 971-972, 1991.10.2337/diacare.15.7.815M. I. Harris, R. Klein, T. A. Welborn, and M. W. Knuiman, "Onset of NIDDM occurs at least 4-7 yr before clinical diagnosis," Diabetes Care, vol. 15, no. 7, pp. 815-819, 1992.10.1056/NEJMoa012512W. C. Knowler et al., "Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin.," N. Engl. J. Med., vol. 346, no. 6, pp. 393-403, 2002.B. Paulweber et al., "A European evidence-based guideline for the prevention of type 2 diabetes.," Horm. Metab. Res. Horm. Stoffwechselforschung= Horm. Metab., vol. 42, no. S 01, pp. S3-36, 2010.10.1055/s-0028-1087203P. E. H. Schwarz, J. Li, J. Lindstrom, and J. Tuomilehto, "Tools for predicting the risk of type 2 diabetes in daily practice," Horm. Metab. Res., vol. 41, no. 02, pp. 86-97, 2009.10.1146/annurev.bioeng.8.061505.095802P. Sajda, "Machine learning for detection and diagnosis of disease," Annu. Rev. Biomed. Eng., vol. 8, pp. 537-565, 2006.10.1186/s12911-019-0918-5A. Dinh, S. Miertschin, A. Young, and S. D. Mohanty, "A data-driven approach to predicting diabetes and cardiovascular disease with machine learning," BMC Med. Inform. Decis. Mak., vol. 19, no. 1, pp. 1-15, 2019, doi: 10.1186/s12911-019-0918-5.R. A. Wilson and F. C. Keil, The MIT encyclopedia of the cognitive sciences. MIT press, 2001.U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth, "From data mining to knowledge discovery in databases," AI Mag., vol. 17, no. 3, p. 37, 1996.S. Russell and P. Norvig, "Artificial intelligence: a modern approach," 2002.10.7551/mitpress/13811.001.0001E. Alpaydin, Introduction to machine learning. MIT press, 2020.10.2337/diacare.28.suppl_1.S37D. Mellitus, "Diagnosis and classification of diabetes mellitus," Diabetes Care, vol. 28, no. S37, pp. S5-S10, 2005.10.1177/193229681100500127E. J. Caveney and O. J. Cohen, "Diabetes and biomarkers," J. Diabetes Sci. Technol., vol. 5, no. 1, pp. 192-197, 2011.10.1016/j.compbiomed.2016.05.005H. F. Jelinek, A. Stranieri, A. Yatsko, and S. Venkatraman, "Data analytics identify glycated haemoglobin co-markers for type 2 diabetes mellitus diagnosis," Comput. Biol. Med., vol. 75, pp. 90-97, 2016.10.1016/j.cmpb.2017.09.004M. Maniruzzaman et al., "Comparative approaches for classification of diabetes mellitus data: Machine learning paradigm," Comput. Methods Programs Biomed., vol. 152, pp. 23-34, 2017, doi: 10.1016/j.cmpb.2017.09.004.10.1016/j.jclinepi.2015.10.002F. Bagherzadeh-Khiabani, A. Ramezankhani, F. Azizi, F. Hadaegh, E. W. Steyerberg, and D. Khalili, "A tutorial on variable selection for clinical prediction models: feature selection methods in data mining could improve the results," J. Clin. Epidemiol., vol. 71, pp. 76-85, 2016.10.1007/s11517-015-1263-1E. I. Georga, V. C. Protopappas, D. Polyzos, and D. I. Fotiadis, "Evaluation of short-term predictors of glucose concentration in type 1 diabetes combining feature ranking with regression models," Med. Biol. Eng. Comput., vol. 53, no. 12, pp. 1305-1318, 2015.10.1016/j.jbi.2015.02.001K.-J. Wang, A. M. Adrian, K.-H. Chen, and K.-M. Wang, "An improved electromagnetism-like mechanism algorithm and its application to the prediction of diabetes mellitus," J. Biomed. Inform., vol. 54, pp. 220-229, 2015.10.1016/j.eswa.2013.04.003M. W. Aslam, Z. Zhu, and A. K. Nandi, "Feature generation using genetic programming with comparative partner selection for diabetes classification," Expert Syst. Appl., vol. 40, no. 13, pp. 5402-5412, 2013.10.1016/j.compbiomed.2016.04.014C. Sideris, M. Pourhomayoun, H. Kalantarian, and M. Sarrafzadeh, "A flexible data-driven comorbidity feature extraction framework," Comput. Biol. Med., vol. 73, pp. 165-172, 2016.10.1177/0272989X14560647A. Ramezankhani, O. Pournik, J. Shahrabi, F. Azizi, F. Hadaegh, and D. Khalili, "The impact of oversampling with SMOTE on the performance of 3 classifiers in prediction of type 2 diabetes," Med. Decis. Mak., vol. 36, no. 1, pp. 137-144, 2016.10.1109/I-SMAC.2017.8058253G. D. Kalyankar, S. R. Poojara, and N. V. Dharwadkar, "Predictive analysis of diabetic patient data using machine learning and Hadoop," Proc. Int. Conf. IoT Soc. Mobile, Anal. Cloud, I-SMAC 2017, no. Dm, pp. 619-624, 2017, doi: 10.1109/I-SMAC.2017.8058253.10.1016/j.eswa.2011.01.017D. Çalişir and E. Doğantekin, "An automatic diabetes diagnosis system based on LDA-Wavelet Support Vector Machine Classifier," Expert Syst. Appl., vol. 38, no. 7, pp. 8311-8315, 2011.10.1016/j.eswa.2011.05.018M. F. Ganji and M. S. Abadeh, "A fuzzy classification system based on Ant Colony Optimization for diabetes disease diagnosis," Expert Syst. Appl., vol. 38, no. 12, pp. 14650-14659, 2011.10.1109/TITB.2012.2219876E. I. Georga et al., "Multivariate prediction of subcutaneous glucose concentration in type 1 diabetes patients based on support vector regression," IEEE J. Biomed. Heal. informatics, vol. 17, no. 1, pp. 71-81, 2012.10.1093/jamia/ocw028V. Agarwal et al., "Learning statistical models of phenotypes using noisy labeled training data," J. Am. Med. Informatics Assoc., vol. 23, no. 6, pp. 1166-1173, 2016.10.1016/j.artmed.2015.08.003S. El-Sappagh, M. Elmogy, and A. M. Riad, "A fuzzy-ontology-oriented case-based reasoning framework for semantic diabetes diagnosis," Artif. Intell. Med., vol. 65, no. 3, pp. 179-208, 2015.10.1007/s00146-013-0456-0A. Sarwar and V. Sharma, "Comparative analysis of machine learning techniques in prognosis of type II diabetes," AI Soc., vol. 29, no. 1, pp. 123-129, 2014, doi: 10.1007/s00146-013-0456-0.10.1109/TBME.2013.2282625B. Zhang, B. V. K. Vijaya Kumar, and D. Zhang, "Detecting diabetes mellitus and nonproliferative diabetic retinopathy using tongue color, texture, and geometry features," IEEE Trans. Biomed. Eng., vol. 61, no. 2, pp. 491-501, 2014, doi: 10.1109/TBME.2013.2282625.10.5120/ijca2017916020M. Aminul and N. Jahan, "Prediction of Onset Diabetes using Machine Learning Techniques," Int. J. Comput. Appl., vol. 180, no. 5, pp. 7-11, 2017, doi: 10.5120/ijca2017916020.10.1089/big.2015.0020N. Razavian, S. Blecker, A. M. Schmidt, A. Smith-McLallen, S. Nigam, and D. Sontag, "Population-level prediction of type 2 diabetes from claims data and analysis of risk factors," Big Data, vol. 3, no. 4, pp. 277-287, 2015.H. Núñez, C. Angulo, and A. Català, "Rule extraction from support vector machines.," in Esann, 2002, pp. 107-112.10.1016/j.jbi.2015.12.001S. Bashir, U. Qamar, and F. H. Khan, "IntelliHealth: a medical decision support application using a novel weighted multi-layer classifier ensemble framework," J. Biomed. Inform., vol. 59, pp. 185-200, 2016.10.1016/j.cmpb.2011.03.018A. Ozcift and A. Gulten, "Classifier ensemble construction with rotation forest to improve medical diagnosis performance of machine learning algorithms," Comput. Methods Programs Biomed., vol. 104, no. 3, pp. 443-451, 2011.10.1111/j.1468-0394.2010.00527.xE. D. Übeyli, "Automatic diagnosis of diabetes using adaptive neuro‐fuzzy inference systems," Expert Syst., vol. 27, no. 4, pp. 259-266, 2010.10.1007/978-3-642-13208-7_52M. Kordos, M. Blachnik, and D. Strzempa, "Do we need whatever more than k-NN?," in International Conference on Artificial Intelligence and Soft Computing, 2010, pp. 414-421.10.1109/TBME.2012.2188893C. Zecchin, A. Facchinetti, G. Sparacino, G. De Nicolao, and C. Cobelli, "Neural network incorporating meal information improves accuracy of short-time prediction of glucose concentration," IEEE Trans. Biomed. Eng., vol. 59, no. 6, pp. 1550-1560, 2012.10.1186/s12884-019-2374-8T. Zheng et al., "A simple model to predict risk of gestational diabetes mellitus from 8 to 20 weeks of gestation in Chinese women," BMC Pregnancy Childbirth, vol. 19, no. 1, pp. 1-10, 2019, doi: 10.1186/s12884-019-2374-8.10.1109/ICCMC.2019.8819841P. Sonar and K. Jaya Malini, "Diabetes prediction using different machine learning approaches," Proc. 3rd Int. Conf. Comput. Me'thodol. Commun. ICCMC 2019, no. Iccmc, pp. 367-371, 2019, doi: 10.1109/ICCMC.2019.8819841.10.1186/s40537-019-0175-6N. Sneha and T. Gangil, "Analysis of diabetes mellitus for early prediction using optimal features selection," J. Big Data, vol. 6, no. 1, 2019, doi: 10.1186/s40537-019-0175-6.10.1109/UBMYK48245.2019.8965542A. Al-Zebari and A. Sengur, "Performance Comparison of Machine Learning Techniques on Diabetes Disease Detection," 1st Int. Informatics Softw. Eng. Conf. Innov. Technol. Digit. Transform. IISEC 2019 - Proc., pp. 2-5, 2019, doi: 10.1109/UBMYK48245.2019.8965542.K. M. Varma and Dr. B.S. Panda, "Comparative analysis of Predicting Diabetes Using Machine Learning Techniques," J. Emerg. Technol. Innov. Res., vol. 6, no. 6, pp. 522-530, 2019, [Online]. Available: www.jetir.org.10.5888/pcd16.190109Z. Xie, O. Nikolayeva, J. Luo, and D. Li, "Building risk prediction models for type 2 diabetes using machine learning techniques," Prev. Chronic Dis., vol. 16, no. 9, pp. 1-9, 2019, doi: 10.5888/pcd16.190109.10.1016/j.procs.2020.01.047A. Mujumdar and V. Vaidehi, "Diabetes Prediction using Machine Learning Algorithms," Procedia Comput. Sci., vol. 165, pp. 292-299, 2019, doi: 10.1016/j.procs.2020.01.047.10.1210/clinem/dgaa899H. H. Wu YT, Zhang CJ, Mol BW, Kawai A, Li C, Chen L, Wang Y, Sheng JZ, Fan JX, Shi Y, "Early prediction of gestational diabetes mellitus in the Chinese population via advanced machine learning," J Clin Endocrinol Metab, no. 301, pp. 1-27, 2020, doi: 10.1210/clinem/dgaa899.10.1186/s12911-020-01318-4J. Ye, L. Yao, J. Shen, R. Janarthanam, and Y. Luo, "Predicting mortality in critically ill patients with diabetes using machine learning and clinical notes," BMC Med. Inform. Decis. Mak., vol. 20, no. 11, pp. 1-8, 2020, doi: 10.1186/s12911-020-01318-4.B. Pranto, S. M. Mehnaz, E. B. Mahid, I. M. Sadman, A. Rahman, and S. Momen, "Evaluating10.35940/ijitee.E2692.039520A. S. Hassan, I. Malaserene, and A. A. Leema, "Diabetes Mellitus Prediction using Classification Techniques," Int. J. Innov. Technol. Explor. Eng., vol. 9, no. 5, pp. 2080-2084, 2020, doi: 10.35940/ijitee.e2692.039520.S. Rani, "mining in Continuous data for Diabetes Prediction," 2018 Second Int. Conf. Intell. Comput. Control Syst., no. Iciccs, pp. 1209-1214, 2018.P. R. K. Varma, V. V. Kumari, and S. S. Kumar, Classification of Diabetes Mellitus Disease (DMD): A Data Mining (DM) Approach, vol. 710, no. Dmd. Springer Singapore, 2018.10.1145/3433996.3434025F. Hou, Z. X. Cheng, L. Y. Kang, and W. Zheng, "Prediction of Gestational Diabetes Based on LightGBM," ACM Int. Conf. Proceeding Ser., pp. 161-165, 2020, doi: 10.1145/3433996.3434025.10.1002/dmrr.3397H. Liu et al., "Machine learning risk score for prediction of gestational diabetes in early pregnancy in Tianjin, China," Diabetes. Metab. Res. Rev., no. February, 2020, doi: 10.1002/dmrr.3397.10.1155/2020/4168340Y. Ye, Y. Xiong, Q. Zhou, J. Wu, X. Li, and X. Xiao, "Comparison of Machine Learning Methods and Conventional Logistic Regressions for Predicting Gestational Diabetes Using Routine Clinical Data: A Retrospective Cohort Study," J. Diabetes Res., vol. 2020, 2020, [Online]. Available: https://www.hindawi.com/journals/jdr/2020/4168340/.10.2196/21573J. Shen et al., "An innovative artificial intelligence-based app for the diagnosis of gestational diabetes mellitus (GDM-AI): Development study," J. Med. Internet Res., vol. 22, no. 9, pp. 1-11, 2020, doi: 10.2196/21573.10.1038/s41598-020-61123-xL. Zhang, Y. Wang, M. Niu, C. Wang, and Z. Wang, "Machine learning for characterizing risk of type 2 diabetes mellitus in a rural Chinese population: the Henan Rural Cohort Study," Sci. Rep., vol. 10, no. 1, pp. 1-10, 2020, doi: 10.1038/s41598-020-61123-x.10.1109/ACCESS.2020.3042483E. A. Pustozerov et al., "Machine Learning Approach for Postprandial Blood Glucose Prediction in Gestational Diabetes Mellitus," IEEE Access, vol. 8, 2020, doi: 10.1109/ACCESS.2020.3042483.AUTHORS PROFILEKumar R, has Completed his B.E and M.E in Computer Science and Engineering in Anna University with First Class and Distinction. He is currently pursuing his research in Annamalai University, Chidambaram, Tamil Nadu., in the area of Data Mining. His research interest includes Data Mining, Pattern Classifications. He is also working as Assistant Professor in the Department of Information Science and Engineering in MVJ College of Engineering, Bangalore, he has more than 10 years of teaching experience in engineering college.Dr S Pazhanirajan has Completed his B.E and M.E in Computer Science and Engineering in Annamalai University He is currently working as Assistant Professor in the Department of Computer Science and Engineering, Annamalai University, Chidambaram, Tamil Nadu. His Research interest includes Data Mining, Pattern Classification, Audio and Image Processing. He has more than 14 years of Experience in Teaching and has more than 10 publications in reputed journals.10.1038/498255aV. Marx, "The big challenges of big data," Nature, vol. 498, no. 7453, pp. 255-260, 2013.10.1038/493473aC. A. Mattmann, "A vision for data science," Nature, vol. 493, no. 7433, pp. 473-475, 2013.10.1146/annurev.nutr.23.011702.073212L. S. Lieberman, "Dietary, evolutionary, and modernizing influences on the prevalence of type 2 diabetes," Annu. Rev. Nutr., vol. 23, no. 1, pp. 345-377, 2003.10.1111/j.1464-5491.1991.tb01540.xD. M. A. Jackson, R. Wills, J. Davies, K. Meadows, B. M. Singh, and P. H. Wise, "Public awareness of the symptoms of diabetes mellitus," Diabet. Med., vol. 8, no. 10, pp. 971-972, 1991.10.2337/diacare.15.7.815M. I. Harris, R. Klein, T. A. Welborn, and M. W. Knuiman, "Onset of NIDDM occurs at least 4-7 yr before clinical diagnosis," Diabetes Care, vol. 15, no. 7, pp. 815-819, 1992.10.1056/NEJMoa012512W. C. Knowler et al., "Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin.," N. Engl. J. Med., vol. 346, no. 6, pp. 393-403, 2002.B. Paulweber et al., "A European evidence-based guideline for the prevention of type 2 diabetes.," Horm. Metab. Res. Horm. Stoffwechselforschung= Horm. Metab., vol. 42, no. S 01, pp. S3-36, 2010.10.1055/s-0028-1087203P. E. H. Schwarz, J. Li, J. Lindstrom, and J. Tuomilehto, "Tools for predicting the risk of type 2 diabetes in daily practice," Horm. Metab. Res., vol. 41, no. 02, pp. 86-97, 2009.10.1146/annurev.bioeng.8.061505.095802P. Sajda, "Machine learning for detection and diagnosis of disease," Annu. Rev. Biomed. Eng., vol. 8, pp. 537-565, 2006.10.1186/s12911-019-0918-5A. Dinh, S. Miertschin, A. Young, and S. D. Mohanty, "A data-driven approach to predicting diabetes and cardiovascular disease with machine learning," BMC Med. Inform. Decis. Mak., vol. 19, no. 1, pp. 1-15, 2019, doi: 10.1186/s12911-019-0918-5.R. A. Wilson and F. C. Keil, The MIT encyclopedia of the cognitive sciences. MIT press, 2001.U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth, "From data mining to knowledge discovery in databases," AI Mag., vol. 17, no. 3, p. 37, 1996.S. Russell and P. Norvig, "Artificial intelligence: a modern approach," 2002.10.7551/mitpress/13811.001.0001E. Alpaydin, Introduction to machine learning. MIT press, 2020.10.2337/diacare.28.suppl_1.S37D. Mellitus, "Diagnosis and classification of diabetes mellitus," Diabetes Care, vol. 28, no. S37, pp. S5-S10, 2005.10.1177/193229681100500127E. J. Caveney and O. J. Cohen, "Diabetes and biomarkers," J. Diabetes Sci. Technol., vol. 5, no. 1, pp. 192-197, 2011.10.1016/j.compbiomed.2016.05.005H. F. Jelinek, A. Stranieri, A. Yatsko, and S. Venkatraman, "Data analytics identify glycated haemoglobin co-markers for type 2 diabetes mellitus diagnosis," Comput. Biol. Med., vol. 75, pp. 90-97, 2016.10.1016/j.cmpb.2017.09.004M. Maniruzzaman et al., "Comparative approaches for classification of diabetes mellitus data: Machine learning paradigm," Comput. Methods Programs Biomed., vol. 152, pp. 23-34, 2017, doi: 10.1016/j.cmpb.2017.09.004.10.1016/j.jclinepi.2015.10.002F. Bagherzadeh-Khiabani, A. Ramezankhani, F. Azizi, F. Hadaegh, E. W. Steyerberg, and D. Khalili, "A tutorial on variable selection for clinical prediction models: feature selection methods in data mining could improve the results," J. Clin. Epidemiol., vol. 71, pp. 76-85, 2016.10.1007/s11517-015-1263-1E. I. Georga, V. C. Protopappas, D. Polyzos, and D. I. Fotiadis, "Evaluation of short-term predictors of glucose concentration in type 1 diabetes combining feature ranking with regression models," Med. Biol. Eng. Comput., vol. 53, no. 12, pp. 1305-1318, 2015.10.1016/j.jbi.2015.02.001K.-J. Wang, A. M. Adrian, K.-H. Chen, and K.-M. Wang, "An improved electromagnetism-like mechanism algorithm and its application to the prediction of diabetes mellitus," J. Biomed. Inform., vol. 54, pp. 220-229, 2015.10.1016/j.eswa.2013.04.003M. W. Aslam, Z. Zhu, and A. K. Nandi, "Feature generation using genetic programming with comparative partner selection for diabetes classification," Expert Syst. Appl., vol. 40, no. 13, pp. 5402-5412, 2013.10.1016/j.compbiomed.2016.04.014C. Sideris, M. Pourhomayoun, H. Kalantarian, and M. Sarrafzadeh, "A flexible data-driven comorbidity feature extraction framework," Comput. Biol. Med., vol. 73, pp. 165-172, 2016.10.1177/0272989X14560647A. Ramezankhani, O. Pournik, J. Shahrabi, F. Azizi, F. Hadaegh, and D. Khalili, "The impact of oversampling with SMOTE on the performance of 3 classifiers in prediction of type 2 diabetes," Med. Decis. Mak., vol. 36, no. 1, pp. 137-144, 2016.10.1109/I-SMAC.2017.8058253G. D. Kalyankar, S. R. Poojara, and N. V. Dharwadkar, "Predictive analysis of diabetic patient data using machine learning and Hadoop," Proc. Int. Conf. IoT Soc. Mobile, Anal. Cloud, I-SMAC 2017, no. Dm, pp. 619-624, 2017, doi: 10.1109/I-SMAC.2017.8058253.10.1016/j.eswa.2011.01.017D. Çalişir and E. Doğantekin, "An automatic diabetes diagnosis system based on LDA-Wavelet Support Vector Machine Classifier," Expert Syst. Appl., vol. 38, no. 7, pp. 8311-8315, 2011.10.1016/j.eswa.2011.05.018M. F. Ganji and M. S. Abadeh, "A fuzzy classification system based on Ant Colony Optimization for diabetes disease diagnosis," Expert Syst. Appl., vol. 38, no. 12, pp. 14650-14659, 2011.10.1109/TITB.2012.2219876E. I. Georga et al., "Multivariate prediction of subcutaneous glucose concentration in type 1 diabetes patients based on support vector regression," IEEE J. Biomed. Heal. informatics, vol. 17, no. 1, pp. 71-81, 2012.10.1093/jamia/ocw028V. Agarwal et al., "Learning statistical models of phenotypes using noisy labeled training data," J. Am. Med. Informatics Assoc., vol. 23, no. 6, pp. 1166-1173, 2016.10.1016/j.artmed.2015.08.003S. El-Sappagh, M. Elmogy, and A. M. Riad, "A fuzzy-ontology-oriented case-based reasoning framework for semantic diabetes diagnosis," Artif. Intell. Med., vol. 65, no. 3, pp. 179-208, 2015.10.1007/s00146-013-0456-0A. Sarwar and V. Sharma, "Comparative analysis of machine learning techniques in prognosis of type II diabetes," AI Soc., vol. 29, no. 1, pp. 123-129, 2014, doi: 10.1007/s00146-013-0456-0.10.1109/TBME.2013.2282625B. Zhang, B. V. K. Vijaya Kumar, and D. Zhang, "Detecting diabetes mellitus and nonproliferative diabetic retinopathy using tongue color, texture, and geometry features," IEEE Trans. Biomed. Eng., vol. 61, no. 2, pp. 491-501, 2014, doi: 10.1109/TBME.2013.2282625.10.5120/ijca2017916020M. Aminul and N. 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Methods Programs Biomed., vol. 104, no. 3, pp. 443-451, 2011.10.1111/j.1468-0394.2010.00527.xE. D. Übeyli, "Automatic diagnosis of diabetes using adaptive neuro‐fuzzy inference systems," Expert Syst., vol. 27, no. 4, pp. 259-266, 2010.10.1007/978-3-642-13208-7_52M. Kordos, M. Blachnik, and D. Strzempa, "Do we need whatever more than k-NN?," in International Conference on Artificial Intelligence and Soft Computing, 2010, pp. 414-421.10.1109/TBME.2012.2188893C. Zecchin, A. Facchinetti, G. Sparacino, G. De Nicolao, and C. Cobelli, "Neural network incorporating meal information improves accuracy of short-time prediction of glucose concentration," IEEE Trans. Biomed. Eng., vol. 59, no. 6, pp. 1550-1560, 2012.10.1186/s12884-019-2374-8T. Zheng et al., "A simple model to predict risk of gestational diabetes mellitus from 8 to 20 weeks of gestation in Chinese women," BMC Pregnancy Childbirth, vol. 19, no. 1, pp. 1-10, 2019, doi: 10.1186/s12884-019-2374-8.10.1109/ICCMC.2019.8819841P. Sonar and K. Jaya Malini, "Diabetes prediction using different machine learning approaches," Proc. 3rd Int. Conf. Comput. Me'thodol. Commun. ICCMC 2019, no. Iccmc, pp. 367-371, 2019, doi: 10.1109/ICCMC.2019.8819841.10.1186/s40537-019-0175-6N. Sneha and T. Gangil, "Analysis of diabetes mellitus for early prediction using optimal features selection," J. Big Data, vol. 6, no. 1, 2019, doi: 10.1186/s40537-019-0175-6.10.1109/UBMYK48245.2019.8965542A. Al-Zebari and A. Sengur, "Performance Comparison of Machine Learning Techniques on Diabetes Disease Detection," 1st Int. Informatics Softw. Eng. Conf. Innov. Technol. Digit. Transform. IISEC 2019 - Proc., pp. 2-5, 2019, doi: 10.1109/UBMYK48245.2019.8965542.K. M. Varma and Dr. B.S. Panda, "Comparative analysis of Predicting Diabetes Using Machine Learning Techniques," J. Emerg. Technol. Innov. Res., vol. 6, no. 6, pp. 522-530, 2019, [Online]. Available: www.jetir.org.10.5888/pcd16.190109Z. Xie, O. Nikolayeva, J. Luo, and D. Li, "Building risk prediction models for type 2 diabetes using machine learning techniques," Prev. Chronic Dis., vol. 16, no. 9, pp. 1-9, 2019, doi: 10.5888/pcd16.190109.10.1016/j.procs.2020.01.047A. Mujumdar and V. Vaidehi, "Diabetes Prediction using Machine Learning Algorithms," Procedia Comput. Sci., vol. 165, pp. 292-299, 2019, doi: 10.1016/j.procs.2020.01.047.10.1210/clinem/dgaa899H. H. Wu YT, Zhang CJ, Mol BW, Kawai A, Li C, Chen L, Wang Y, Sheng JZ, Fan JX, Shi Y, "Early prediction of gestational diabetes mellitus in the Chinese population via advanced machine learning," J Clin Endocrinol Metab, no. 301, pp. 1-27, 2020, doi: 10.1210/clinem/dgaa899.10.1186/s12911-020-01318-4J. Ye, L. Yao, J. Shen, R. Janarthanam, and Y. Luo, "Predicting mortality in critically ill patients with diabetes using machine learning and clinical notes," BMC Med. Inform. Decis. Mak., vol. 20, no. 11, pp. 1-8, 2020, doi: 10.1186/s12911-020-01318-4.B. Pranto, S. M. Mehnaz, E. B. Mahid, I. M. Sadman, A. Rahman, and S. Momen, "Evaluating10.35940/ijitee.E2692.039520A. S. Hassan, I. Malaserene, and A. A. Leema, "Diabetes Mellitus Prediction using Classification Techniques," Int. J. Innov. Technol. Explor. Eng., vol. 9, no. 5, pp. 2080-2084, 2020, doi: 10.35940/ijitee.e2692.039520.S. Rani, "mining in Continuous data for Diabetes Prediction," 2018 Second Int. Conf. Intell. Comput. Control Syst., no. Iciccs, pp. 1209-1214, 2018.P. R. K. Varma, V. V. Kumari, and S. S. Kumar, Classification of Diabetes Mellitus Disease (DMD): A Data Mining (DM) Approach, vol. 710, no. Dmd. Springer Singapore, 2018.10.1145/3433996.3434025F. Hou, Z. X. Cheng, L. Y. Kang, and W. Zheng, "Prediction of Gestational Diabetes Based on LightGBM," ACM Int. Conf. Proceeding Ser., pp. 161-165, 2020, doi: 10.1145/3433996.3434025.10.1002/dmrr.3397H. Liu et al., "Machine learning risk score for prediction of gestational diabetes in early pregnancy in Tianjin, China," Diabetes. Metab. Res. Rev., no. February, 2020, doi: 10.1002/dmrr.3397.10.1155/2020/4168340Y. Ye, Y. Xiong, Q. Zhou, J. Wu, X. Li, and X. Xiao, "Comparison of Machine Learning Methods and Conventional Logistic Regressions for Predicting Gestational Diabetes Using Routine Clinical Data: A Retrospective Cohort Study," J. Diabetes Res., vol. 2020, 2020, [Online]. Available: https://www.hindawi.com/journals/jdr/2020/4168340/.10.2196/21573J. Shen et al., "An innovative artificial intelligence-based app for the diagnosis of gestational diabetes mellitus (GDM-AI): Development study," J. Med. Internet Res., vol. 22, no. 9, pp. 1-11, 2020, doi: 10.2196/21573.10.1038/s41598-020-61123-xL. Zhang, Y. Wang, M. Niu, C. Wang, and Z. Wang, "Machine learning for characterizing risk of type 2 diabetes mellitus in a rural Chinese population: the Henan Rural Cohort Study," Sci. Rep., vol. 10, no. 1, pp. 1-10, 2020, doi: 10.1038/s41598-020-61123-x.10.1109/ACCESS.2020.3042483E. A. Pustozerov et al., "Machine Learning Approach for Postprandial Blood Glucose Prediction in Gestational Diabetes Mellitus," IEEE Access, vol. 8, 2020, doi: 10.1109/ACCESS.2020.3042483.AUTHORS PROFILEKumar R, has Completed his B.E and M.E in Computer Science and Engineering in Anna University with First Class and Distinction. He is currently pursuing his research in Annamalai University, Chidambaram, Tamil Nadu., in the area of Data Mining. His research interest includes Data Mining, Pattern Classifications. He is also working as Assistant Professor in the Department of Information Science and Engineering in MVJ College of Engineering, Bangalore, he has more than 10 years of teaching experience in engineering college.Dr S Pazhanirajan has Completed his B.E and M.E in Computer Science and Engineering in Annamalai University He is currently working as Assistant Professor in the Department of Computer Science and Engineering, Annamalai University, Chidambaram, Tamil Nadu. His Research interest includes Data Mining, Pattern Classification, Audio and Image Processing. He has more than 14 years of Experience in Teaching and has more than 10 publications in reputed journals.10.1038/498255aV. Marx, "The big challenges of big data," Nature, vol. 498, no. 7453, pp. 255-260, 2013.10.1038/493473aC. A. Mattmann, "A vision for data science," Nature, vol. 493, no. 7433, pp. 473-475, 2013.10.1146/annurev.nutr.23.011702.073212L. S. Lieberman, "Dietary, evolutionary, and modernizing influences on the prevalence of type 2 diabetes," Annu. Rev. Nutr., vol. 23, no. 1, pp. 345-377, 2003.10.1111/j.1464-5491.1991.tb01540.xD. M. A. Jackson, R. Wills, J. Davies, K. Meadows, B. M. Singh, and P. H. Wise, "Public awareness of the symptoms of diabetes mellitus," Diabet. Med., vol. 8, no. 10, pp. 971-972, 1991.10.2337/diacare.15.7.815M. I. Harris, R. Klein, T. A. Welborn, and M. W. Knuiman, "Onset of NIDDM occurs at least 4-7 yr before clinical diagnosis," Diabetes Care, vol. 15, no. 7, pp. 815-819, 1992.10.1056/NEJMoa012512W. C. Knowler et al., "Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin.," N. Engl. J. Med., vol. 346, no. 6, pp. 393-403, 2002.B. Paulweber et al., "A European evidence-based guideline for the prevention of type 2 diabetes.," Horm. Metab. Res. Horm. Stoffwechselforschung= Horm. Metab., vol. 42, no. S 01, pp. S3-36, 2010.10.1055/s-0028-1087203P. E. H. Schwarz, J. Li, J. Lindstrom, and J. Tuomilehto, "Tools for predicting the risk of type 2 diabetes in daily practice," Horm. Metab. Res., vol. 41, no. 02, pp. 86-97, 2009.10.1146/annurev.bioeng.8.061505.095802P. Sajda, "Machine learning for detection and diagnosis of disease," Annu. Rev. Biomed. Eng., vol. 8, pp. 537-565, 2006.10.1186/s12911-019-0918-5A. Dinh, S. Miertschin, A. Young, and S. D. Mohanty, "A data-driven approach to predicting diabetes and cardiovascular disease with machine learning," BMC Med. Inform. Decis. Mak., vol. 19, no. 1, pp. 1-15, 2019, doi: 10.1186/s12911-019-0918-5.R. A. Wilson and F. C. Keil, The MIT encyclopedia of the cognitive sciences. MIT press, 2001.U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth, "From data mining to knowledge discovery in databases," AI Mag., vol. 17, no. 3, p. 37, 1996.S. Russell and P. Norvig, "Artificial intelligence: a modern approach," 2002.10.7551/mitpress/13811.001.0001E. Alpaydin, Introduction to machine learning. MIT press, 2020.10.2337/diacare.28.suppl_1.S37D. Mellitus, "Diagnosis and classification of diabetes mellitus," Diabetes Care, vol. 28, no. S37, pp. S5-S10, 2005.10.1177/193229681100500127E. J. Caveney and O. J. Cohen, "Diabetes and biomarkers," J. Diabetes Sci. Technol., vol. 5, no. 1, pp. 192-197, 2011.10.1016/j.compbiomed.2016.05.005H. F. Jelinek, A. Stranieri, A. Yatsko, and S. Venkatraman, "Data analytics identify glycated haemoglobin co-markers for type 2 diabetes mellitus diagnosis," Comput. Biol. Med., vol. 75, pp. 90-97, 2016.10.1016/j.cmpb.2017.09.004M. Maniruzzaman et al., "Comparative approaches for classification of diabetes mellitus data: Machine learning paradigm," Comput. Methods Programs Biomed., vol. 152, pp. 23-34, 2017, doi: 10.1016/j.cmpb.2017.09.004.10.1016/j.jclinepi.2015.10.002F. Bagherzadeh-Khiabani, A. Ramezankhani, F. Azizi, F. Hadaegh, E. W. Steyerberg, and D. Khalili, "A tutorial on variable selection for clinical prediction models: feature selection methods in data mining could improve the results," J. Clin. Epidemiol., vol. 71, pp. 76-85, 2016.10.1007/s11517-015-1263-1E. I. Georga, V. C. Protopappas, D. Polyzos, and D. I. Fotiadis, "Evaluation of short-term predictors of glucose concentration in type 1 diabetes combining feature ranking with regression models," Med. Biol. Eng. 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