[
Al-Gharabawi, F.W. & Abu-Naser, S.S. (2023). Machine Learning-Based Diabetes Prediction: Feature Analysis and Model Assessment. International Journal of Academic Engineering Research (IJAER) 7 (9), 10-17.
]Search in Google Scholar
[
Alehegn, M., Joshi, R.R. & Mulay, P. (2019). Diabetes analysis and prediction using random forest, KNN, Naïve Bayes, and J48: an ensemble approach. Int J Sci Technol Res., 8(9), 1346–1354.
]Search in Google Scholar
[
Amour Diwani, S. & Sam, A. (2014). Diabetes forecasting using supervised learning techniques. Adv. Comput. Sci.: Int. J., 3(5), 10–18, Retrieved from: http://www.acsij.org/acsij/article/view/156.
]Search in Google Scholar
[
Anuja Kumari, V. & Chitra, R. (2013). Classification of diabetes disease using support vector machine”, Int. J. Eng. Res. Appl., 3, 1797–1801.
]Search in Google Scholar
[
Beghriche, T., Djerioui, M., Brik, Y., Attallah, B. & Belhaouari, S.B. (2021) An Efficient Prediction System for Diabetes Disease Based on Deep Neural Network. Hindawi Complexity. Retrieved at: https://doi.org/10.1155/2021/6053824.
]Search in Google Scholar
[
Daghistani, T. & Alshammari, R. (2020). Comparison of statistical logistic regression and random forest machine learning techniques in predicting diabetes. J. Adv. Inf.Technol., 11(2), 78-83.
]Search in Google Scholar
[
DeFronzo, R.A., Ferrannini, E., Groop, L, Henry, R.R., Herman, W.H., Holst, J.J., Hu, F.B., Kahn, C.R., Raz, I., Shulman, G.I., Simonson, D.C., Testa, M.A. & Weiss, R. (2015). Type 2 diabetes mellitus. Nat Rev Dis Primers, 1,15019. doi:10.1038/nrdp.2015.19.
]Search in Google Scholar
[
Dewangan, A.K. & Agrawal, P. (2015). Classification of diabetes mellitus using machine learning techniques. Int. J. Eng. Appl. Sci., 2(5), 145-148.
]Search in Google Scholar
[
Islam, M.M.F., Ferdousi, R., Rahman, S. & Bushra, H.Y. (2020). Likelihood prediction of diabetes at early stage using data mining techniques, in Gupta M, Konar D, Bhattacharyya S, Biswas S (eds) Computer vision and machine intelligence in medical image analysis. Advances in intelligent systems and computing”, 992, Springer, Singapore, 113–125. Retrieved from: 10.1007/978-981-13-8798-2_12.
]Search in Google Scholar
[
Iyer, A., Jeyalatha, S. & Sumbaly, R. (2015). Diagnosis of Diabetes Using Classification Mining Techniques. International Journal of Data Mining & Knowledge Management Process (IJDKP), 5, 1-14. Retrieved from: https://doi.org/10.5121/ijdkp.2015.5101.
]Search in Google Scholar
[
Madhu, B., Aerranagula, V., Mahomad, R., Ravindernaik, V., Madhavi, K. & Krishna, G. (2023) Techniques of Machine Learning for the Purpose of Predicting Diabetes Risk in PIMA Indians. E3S Web of Conferences, 011. Retrieved at: https://doi.org/10.1051/e3sconf/202343001151.
]Search in Google Scholar
[
Malik, S., Harous, S. & El-Sayed, H. (2021). Comparative Analysis of Machine Learning Algorithms for Early Prediction of Diabetes Mellitus in Women. Modelling and Implementation of Complex Systems”, Springer International Publishing. Retrieved from: https://www.springerprofessional.de/en/comparative-analysis-of-machine-learning-algorithms-for-early-pr/18351326.
]Search in Google Scholar
[
Mujumdar, A. & Vaidehi, V. (2019). Diabetes Prediction using Machine Learning Algorithms. Procedia Computer Science, 165, 292–299.
]Search in Google Scholar
[
Olivera, A.R., Roesler, V., Iochpe, C., Schmidt, M.I., Vigo. Á., Barreto S.M., Duncan, B.B. (2017). Sao Paulo Med J.,135 (3), 234-46.
]Search in Google Scholar
[
Perveen, S., Shahbaz, M., Guergachi, A. & Keshavjee, K. (2016). Performance analysis of data mining classification techniques to predict diabetes. Procedia Comput. Sci., 82, 115–121. Retrieved from https://dspace.library.uvic.ca/bitstream/handle/1828/9390/Keshavjee_Karim_ProcediaComputSci_2016.pdf?sequence=1&isAllowed=y
]Search in Google Scholar
[
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/ Journal of Computer Science. 5(12), 1003-1008.
]Search in Google Scholar
[
Rhee, S.Y., Sung, J.M., Kim, S., Cho, I.J., Lee, S.E. & Chang, H.J. (2019). Development and Validation of a Deep Learning Based Diabetes Prediction System Using a Nationwide Population-Based Cohort. Diabetes Metab J., 45, 515-525. Retrieved at: https://doi.org/10.4093/dmj.2020.0081.
]Search in Google Scholar
[
Sanakal, R. & Jayakumari, S.T. (2014). Prognosis of diabetes using data mining approach-fuzzy C means clustering and support vector machine. Int. J. Comput. TrendsTechnol., 11, 94–98.
]Search in Google Scholar
[
Sen, S.K. & Dash, S. (2014). Application of Meta Learning Algorithms for the Prediction of Diabetes Disease. International Journal of Advance Research in Computer Science and Management Studies, 2, 396-401.
]Search in Google Scholar
[
Sisodia, D. & Sisodia, D.S. (2018). Prediction of diabetes using classification algorithms. Procedia Comput. Sci. 132, 1578–1585.
]Search in Google Scholar
[
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.
]Search in Google Scholar
[
Sridar, K. & Shanthi, D. (2014). Medical diagnosis system for the diabetes mellitus by using back propagation-Apriori algorithms. J. Theor. Appl. Inf. Technol., 68(1), 36-43.
]Search in Google Scholar
[
Tasin, I., Ullah, T., Sanjida, N. & Khan, I.R. (2023). Diabetes prediction using machine learning and explainable AI techniques. Healthc. Technol. Lett., 10, 1–10. DOI: 10.1049/htl2.12039.
]Search in Google Scholar
[
Tigga, N.P. & Garg, S. (2019). Predicting type 2 Diabetes using Logistic Regression. Proceedings of the Fourth International Conference on Microelectronics, Computing and Communication Systems MCCS, Lecture Notes of Electrical Engineering, Springer.
]Search in Google Scholar
[
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. BMC Med. Inform. Decis. Mak. 10(16). Retrieved from doi:10.1186/1472-6947-10-16.
]Search in Google Scholar
[
Zou, Q., Qu, K., Luo, Y., Yin, D., Ju, Y. & Tang, H. (2018). Predicting Diabetes Mellitus with Machine Learning Techniques. Frontiers in genetics, 9, 515. Retrieved from https://www.frontiersin.org/articles/10.3389/fgene.2018.00515/full.
]Search in Google Scholar
