Ahuja, R., Sharma, S.C., Ali, M. (2019). A diabetic disease prediction model based on classification algorithms. <em>Ann Emerg Technol Comput.</em>, 3(3), 44–52. Retrieved from doi: <pub-id pub-id-type="doi"><a href="https://doi.org/10.33166/AETiC.2019.03.005" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.33166/AETiC.2019.03.005</a></pub-id>.
Alehegn, M., Joshi, R.R., Mulay, P. (2019). Diabetes analysis and prediction using random forest, KNN, Naïve Bayes, and J48: an ensemble approach. <em>Int J Sci Technol Res.</em>, 8(9), 1346–1354.
Ameena, R.R., Ashadevi, B. (2020). Predictive analysis of diabetic women patients using R. <em>Peter JD, Fernandes SL (eds) Systems simulation and modeling for cloud computing and big data applications”</em> Elsevier Inc., Amsterdam. Retrieved from <a href="https://doi.org/10.1016/B978-0-12-819779-0.00006-X." target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/B978-0-12-819779-0.00006-X.</a>
Ardern, C.I., Katzmarzyk, P.T., Janssen, I., Church, T.S., Blair, S.N. (2005). Revised Adult Treatment Panel III Guidelines and Cardiovascular Disease Mortality in Men Attending a Preventive Medical Clinic. <em>Circulation</em>. 112(10), 1478–1485. Retrieved from <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://www.ahajournals.org/doi/full/10.1161/CIRCULATIONAHA.105.548198">https://www.ahajournals.org/doi/full/10.1161/CIRCULATIONAHA.105.548198</ext-link>.
Daanouni, O., Cherradi, B., Tmiri, A. (2019). Predicting diabetes diseases using mixed data and supervised machine learning algorithms. <em>Abstracts of the 4th international conference on smart city applications, ACM</em>”, Casablanca. Retrieved from <a href="https://doi.org/10.1145/3368756.3369072." target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1145/3368756.3369072.</a>
Daghistani, T., Alshammari, R. (2020). Comparison of statistical logistic regression and random forest machine learning techniques in predicting diabetes. <em>J. Adv. Inf.Technol.</em>, 11(2), 78-83.
Dalakleidi, K.V., Zarkogianni, K., Karamanos, V.G., Thanopoulou, A.C., Nikita, K.S. (2013). A hybrid genetic algorithm for the selection of the critical features for risk prediction of cardiovascular complications in Type 2 Diabetes patients. <em>Abstracts of the 13th IEEE international conference on BioInformatics and BioEngineering</em>, Chania, 10-13 November 2013. Retrieved from: <a href="https://doi.org/10.1109/BIBE.2013.6701620." target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1109/BIBE.2013.6701620.</a>
Dewangan, A.K., Agrawal, P. (2015). Classification of diabetes mellitus using machine learning techniques. <em>Int. J. Eng. Appl. Sci.</em>, 2(5), 145-148.
Islam, M.M.F., Ferdousi, R., Rahman, S., Bushra, H.Y. (2020). Likelihood prediction of diabetes at early stage using data mining techniques, in <em>Gupta M, Konar D, Bhattacharyya S, Biswas S (eds) Computer vision and machine intelligence in medical image analysis. Advances in intelligent systems and computing</em>”, 992, Springer, Singapore, 113–125. Retrieved from: <a href="https://doi.org/10.1007/978-981-13-8798-2_12." target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1007/978-981-13-8798-2_12.</a>
Jaiswal, V., Negi, A., Pal, T (2021). A review on current advances in machine learning based diabetes prediction, <em>Prim Care Diabetes.</em> 15(3), 435-443. Retrieved from doi:<pub-id pub-id-type="doi"><a href="https://doi.org/10.1016/j.pcd.2021.02.005" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.pcd.2021.02.005</a></pub-id>.
Malik, S., Harous, S., El-Sayed, H. (2021). Comparative Analysis of Machine Learning Algorithms for Early Prediction of Diabetes Mellitus in Women. <em>Modelling and Implementation of Complex Systems</em>”, Springer International Publishing. Retrieved from: <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://www.springerprofessional.de/en/comparative-analysis-of-machine-learning-algorithms-for-early-pr/18351326">https://www.springerprofessional.de/en/comparative-analysis-of-machine-learning-algorithms-for-early-pr/18351326</ext-link>.
Maniruzzaman, M., Rahman, M.J., Al-MehediHasan, M., Suri, H.S., Abedin, Md.M., El-Baz, A., Suri, J.S. (2018). Accurate diabetes risk stratification using machine learning: role of missing value and outliers.. <em>J Med Syst.</em>, 42. Retrieved from: <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="<a href="https://doi.org/10.1007/s10916-018-0940-7" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1007/s10916-018-0940-7</a>">https://doi.org/10.1007/s10916-018-0940-7</ext-link>.
Mary Posonia, A., Vigneshwari, S., Jamuna Rani, D. (2000). Machine Learning based Diabetes Prediction using Decision Tree J48. <em>Proceedings of the Third International Conference on Intelligent Sustainable Systems</em>. Retrieved from DOI:<pub-id pub-id-type="doi"><a href="https://doi.org/10.1109/ICISS49785.2020.9316001" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1109/ICISS49785.2020.9316001</a></pub-id>.
Perveen, S., Shahbaz, M., Guergachi, A., Keshavjee, K. (2016). Performance analysis of data mining classification techniques to predict diabetes. <em>Procedia Comput. Sci.</em>, 82, 115–121. Retrieved from <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://dspace.library.uvic.ca/bitstream/handle/1828/9390/Keshavjee_Karim_ProcediaComputSci_2016.pdf?sequence=1&isAllowed=y">https://dspace.library.uvic.ca/bitstream/handle/1828/9390/Keshavjee_Karim_ProcediaComputSci_2016.pdf?sequence=1&isAllowed=y</ext-link>.
Purnami, S.W., Embong, A., Zainand, J.M., Rahayu, S.P. (2019). A New Smooth Support Vector Machine and Its Applications in Diabetes Disease Diagnosis/ <em>Journal of Computer Science</em>. 5(12), 1003-1008.
Sanakal, R., Jayakumari, S.T. (2014). Prognosis of diabetes using data mining approach-fuzzy C means clustering and support vector machine. <em>Int. J. Comput. TrendsTechnol.</em>, 11, 94–98.
Sen, S.K., Dash, S. (2014). Application of Meta Learning Algorithms for the Prediction of Diabetes Disease. <em>International Journal of Advance Research in Computer Science and Management Studies</em>, 2, 396-401.
Soliman, O.S., AboElhamd, E. (2014). Classification of Diabetes Mellitus using Modified Particle Swarm Optimization and Least Squares Support Vector Machine. Retrieved from arXiv:1405.0549.
Sridar, K., Shanthi, D. (2014). Medical diagnosis system for the diabetes mellitus by using back propagation-Apriori algorithms. <em>J. Theor. Appl. Inf. Technol.</em>, 68(1), 36-43.
Tigga, N.P., Garg, S. (2019). Predicting type 2 Diabetes using Logistic Regression. <em>Proceedings of the Fourth International Conference on Microelectronics, Computing and Communication Systems MCCS</em>, Lecture Notes of Electrical Engineering, Springer.
Yu, W., Liu, T., Valdez, R., Gwinn, M., Khoury, M.J. (2010). Application of support vector machine modeling for prediction of common diseases: the case of diabetes and pre-diabetes. <em>BMC Med. Inform. Decis. Mak.</em> 10(16). Retrieved from doi:<pub-id pub-id-type="doi"><a href="https://doi.org/10.1186/1472-6947-10-16" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1186/1472-6947-10-16</a></pub-id>.