References:
1. Bashir, S., Qamar, U., and Khan, F.H. "Intellihealth: a medical decision support application using a novel weighted multi-layer classifier ensemble framework", J. Biomed. Inform. X, 59, pp. 185-200 (2016).
2. Carter, J.A., Long, C.S., Smith, B.P., et al. "Combining elemental analysis of toenails and machine learning techniques as a non-invasive diagnostic tool for the robust classification of type-2 diabetes", Expert Syst. Appl., 115, pp. 245-255 (2019).
3. Atlas, D. "International diabetes federation", idf Diabetes Atlas, 1st Edn., Brussels, Belgium: International Diabetes Federation (2000).
4. Atlas, D. "International diabetes federation", idf Diabetes Atlas, 2nd Edn., Brussels, Belgium: International Diabetes Federation (2003).
5. Atlas, D. "International diabetes federation", idf Diabetes Atlas, 3rd Edn., Brussels, Belgium: International Diabetes Federation (2006).
6. Atlas, D. "International diabetes federation", idf Diabetes Atlas, 4th Edn., Brussels, Belgium: International Diabetes Federation (2009).
7. Atlas, D. "International diabetes federation", idf Diabetes Atlas, 5th Edn., Brussels, Belgium: International Diabetes Federation (2011).
8. Atlas, D. "International diabetes federation", idf Diabetes Atlas, 6th Edn., Brussels, Belgium: International Diabetes Federation (2013).
9. Atlas, D. "International diabetes federation", idf Diabetes Atlas, 7th Edn., Brussels, Belgium: International Diabetes Federation (2015).
10. Atlas, D. "International diabetes federation", idf Diabetes Atlas, 8th Edn., Brussels, Belgium: International Diabetes Federation (2017).
11. Zilbermint, M. "Diabetes and climate change", J Community Hosp Intern Med Perspect, 10(5), pp. 409- 412 (2020).
12. Huang, Y., McCullagh, P., Black, N., et al. "Feature selection and classification model construction on type 2 diabetic patients data", Artif Intell Med., 41(3), pp. 251-262 (2007).
13. Kahramanli, H. and Allahverdi, N. "Design of a hybrid system for the diabetes and heart diseases", Expert Syst. Appl., 35(1-2), pp. 82-89 (2008).
14. Temurtas, H., Yumusak, N., and Temurtas, F. "A comparative study on diabetes disease diagnosis using neural networks", Expert Syst. Appl., 36(4), pp. 8610- 8615 (2009).
15. Patil, B.M., Joshi, R.C., and Toshniwal, D. "Hybrid prediction model for type-2 diabetic patients", Expert Syst. Appl., 37(12), pp. 8102-8108 (2010).
16. Ganji, M.F. and Abadeh, M.S. "A fuzzy classification system based on antcolony optimization for diabetes disease diagnosis", Expert Syst. Appl., 38(12), pp. 14650-14659 (2011).
17. C alisir, D. and Dogantekin, E. "An automatic diabetes diagnosis system based on lda-wavelet support vector machine classifier", Expert. Syst. Appl., 38(7), pp. 8311-8315 (2011).
18. Aslam, M.W., Zhu, Z., and Nandi, A.K. "Feature generation using genetic programming with comparative partner selection for diabetes classification", Expert Syst. Appl., 40(13), pp. 5402-5412 (2013).
19. Seera, M. and Lim, C.P. "A hybrid intelligent system for medical data classification", Expert Syst. Appl., 41(5), pp. 2239-2249 (2014).
20. Gorza lczany, M.B. and Rudzinski, F. "Interpretable and accurate medical data classification-a multiobjective genetic-fuzzy optimization approach", Expert Syst. Appl., 71, pp. 26-39 (2017).
21. Kavakiotis, I., Tsave, O., Salifoglou, A., et al. "Machine learning and data miningmethods in diabetes research", Comput Struct. Biotechnol. J., 15, pp. 104- 116 (2017).
22. Kumari, S., Kumar, D., and Mittal, M. "An ensemble approach for classification and prediction of diabetes mellitus using soft voting classifier", International Journal of Cognitive Computing in Engineering, 2, pp. 40-46 (2021).
23. Zheng, T., Xie, W., Xu, L., et al. "A machine learningbased framework to identify type 2 diabetes through electronic health records", Int. J. Med. Inform., 97, pp. 120-127 (2017).
24. Gupta, D., Choudhury, A., Gupta, U., et al. "Computational approach to clinical diagnosis of diabetes disease: a comparative study", Multimed Tools Appl., 80, p. 126 (2021).
25. Rahman, M., Islam, D., Mukti, R.J., et al. "A deep learning approach based on convolutional lstm for detecting diabetes", Comput. Biol. Chem., pp. 107- 329 (2020).
26. Pranto, B., Mehnaz, S., Mahid, E.B., et al. "Evaluating machine learning methods for predicting diabetes among female patients in Bangladesh", Information, 11(8), p. 374 (2020).
27. Kannadasan, K., Edla, D.R., and Kuppili, V. "Type 2 diabetes data classification using stacked autoencoders in deep neural networks", Clin Epidemiol Glob Health, 7(4), pp. 530-535 (2019).
28. Patra, R. and Bonomali, K. "Analysis and prediction of pima Indian diabetes dataset using sdknn classifier technique", In IOP Conference Series: Materials Science and Engineering, IOP Publishing, 1070, p. 012059 (2021).
29. Khanam, J.J. and Foo, S.Y. "A comparison of machine learning algorithms for diabetes prediction", ICT Express, 7(4), pp. 432-439 (2021).
30. Kaul, S. and Kumar Y. "Artificial intelligence-based learning techniques for diabetes prediction: Challenges and systematic review", SN Comput. Sci., 1(6), pp. 1- 7 (2020).
31. Sangien, T., Bhat, T., and Khan, M.S. "Diabetes disease prediction using classification algorithms", Internet of Things and Its Applications, Springer, pp. 185-197 (2022).