1. Katzmarzyk P.T., Craig C.L., andGauvin L,Adiposity, Physical Fitness and Incident Diabetes: The Physical Activity Longitudinal Study. Diabetologia, vol. 50, no. 3, pp. 538-544, 2007. 2. Wahl P.W., Savage P.J., Psaty B.M., Orchard T.J., Robbins J.A., andTracy, R.P, Diabetes in Older Adults: Comparison of1997 American Diabetes Association Classification of Diabetes Mellitus with 1985 WHO Classification. The Lancet, vol. 352, no. 9133, pp. 1012-1015, 1998. 3. Rahman M.A., Shoaib S.M., Al Amin, M., Toma, R.N., Moni, M.A., and Awal, M.A. A Bayesian Optimization Framework for the Prediction of Diabetes Mellitus. In 2019 5th International Conference on Advances in Electrical Engineering (ICAEE), IEEE, pp. 357-362, September 2019. 4. DEPERLİOĞLU, Ö. and Utku, K.Ö.S.E. Diabetes Determination using Retraining Neural Network. In 2018 International Conference on Artificial Intelligence and Data Processing (IDAP). IEEE, pp. 1-5, September 2018. 5. Wang Q., Cao W., Guo J., Ren J., Cheng Y., andDavis D.N.DMP_MI: an Effective Diabetes Mellitus Classification Algorithm on Imbalanced Data with Missing Values. IEEE Access, vol. 7, pp. 102232-102238, 2019. 6. Kaur, H. and Batra, S.HPCC: An Ensembled Framework for the Prediction of the Onset of Diabetes. In 2017 4th International Conference on Signal Processing, Computing and Control (ISPCC). IEEE, pp. 216-222, September 2017. 7. Kalyankar G.D., Poojara S.R., andDharwadkar N.V,Predictive Analysis of Diabetic Patient Data using Machine Learning and Hadoop. In 2017 international conference on I-SMAC (IoT in social, mobile, analytics and cloud)(I-SMAC). IEEE, pp. 619-624, February 2017. 8. Wei, S., Zhao, X. and Miao, C, A Comprehensive Exploration to the Machine Learning Techniques for Diabetes Identification. In 2018 IEEE 4th World Forum on Internet of Things (WF-IoT), IEEE, pp. 291-295, February 2018. 9. Mohebbi A., Aradóttir T.B., Johansen A.R., Bengtsson H., Fraccaro M., andMørup M.A Deep Learning Approach to Adherence Detection for Type 2 Diabetics. In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, pp. 2896-2899, July 2017 10. Deperlioğlu, Ö. and Köse, U.Diagnogsis of Diabete Mellitus using Deep Neural Network. In 2018 Medical Technologies National Congress (TIPTEKNO). IEEE, pp. 1-4, November 2018. 11. Ahmed, S.T. and Patil, K.K, An Investigative Study on Motifs Extracted Features on Real Time Big-data Signals. In 2016 International Conference on Emerging Technological Trends (ICETT). IEEE, pp. 1-4, October 2016 12. Wang J., Cao K., Fang C., andChen J.FDFuzz: Applying Feature Detection to Fuzz Deep Learning Systems. International Journal of Performability Engineering, vol. 15, no. 10, pp. 2675, 2019. 13. Ahmed S.T., Priyanka H.K., Attar S. and Patted A.Cataract Density Ratio Analysis under Color Image Processing Approach. In 2017 International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, pp. 178-180, June 2017. |