[1] N. Arora, A. Singh, M.Z.N. Al-Dabagh, and S.K. Maitra, “A novel architecture for diabetes patients’ prediction using K-means clustering and SVM,” Math. Probl. Eng., vol. 2022, pp. 1-9, 2022, doi: 10.1155/2022/4815521.
[2] D. Sisodia and D.S. Sisodia, “Prediction of diabetes using classification algorithms,” Procedia Comput. Sci., vol. 132, pp. 1578-1585, 2018, doi: 10.1016/j.procs.2018.05.122.
[3] Z. Salih Ageed et al., “Comprehensive survey of big data mining approaches in cloud systems,” Qubahan Acad. J., vol. 1, no. 2, pp. 29-38, 2021, doi: 10.48161/qaj.v1n2a46.
[4] W. Haoxiang and S. Smys, “Big data analysis and perturbation using data mining algorithm,” J. Soft Comput. Paradigm, vol. 3, no. 1, pp. 19-28, 2021, doi: 10.36548/jscp.2021.1.003.
[5] H. Wu, S. Yang, Z. Huang, J. He, and X. Wang, “Type 2 diabetes mellitus prediction model based on data mining,” Informat. Med. Unlocked, vol. 10, pp. 100-107, 2018, doi: 10.1016/j.imu.2017.12.006.
[6] M.M.F. Islam, R. Ferdousi, S. Rahman, and H. Bushra, “Likelihood prediction of diabetes at early stage using data mining techniques,” in Computer Vision and Machine Intelligence in Medical Image Analysis. Advances in Intelligent Systems and Computing, vol 992, Springer, Singapore, doi: 10.1007/978-981-13-8798-2_12.
[7] F.G. Woldemichael and S. Menaria, “Prediction of diabetes using data mining techniques,” in Proc. 2nd Int. Conf. Trends Electron. Informat. (ICOEI), 2018, doi: 10.1109/icoei.2018.8553959.
[8] C. Fiarni, E.M. Sipayung, and S. Maemunah, “Analysis and prediction of diabetes complication disease using data mining algorithm,” Procedia Comput. Sci., vol. 161, pp. 449-457, 2019, doi: 10.1016/j.procs.2019.11.144.
[9] A. Aldallal and A.A.A. Al-Moosa, “Using data mining techniques to predict diabetes and heart diseases,” in Proc. 4th Int. Conf. Frontiers Signal Process. (ICFSP), Poitiers, France, 2018, pp. 150-154, doi: 10.1109/ICFSP.2018.8552051.
[10] I. Kavakiotis, O. Tsave, A. Salifoglou, N. Maglaveras, I. Vlahavas, and I. Chouvarda, “Machine learning and data mining methods in diabetes research,” Comput. Struct. Biotechnol. J., vol. 15, pp. 104-116, 2017, doi: 10.1016/j.csbj.2016.12.005.
[11] A. Kumar, P. Kumar, A. Srivastava, A. Kumar, K. Vengatesan, and A. Singhal, “Comparative analysis of data mining techniques to predict heart disease for diabetic patients,” in Advances in Computing and Data Sciences (ICACDS 2020), Communications in Computer and Information Science, vol 1244. Springer, Singapore, 2020, doi: 10.1007/978-981-15-6634-9_46.
[12] T.R. Mahesh et al., “Blended ensemble learning prediction model for strengthening diagnosis and treatment of chronic diabetes disease,” Comput. Intell. Neurosci., vol. 2022, 2022, doi: 10.1155/2022/4451792.
[13] A. Oza and A. Bokhare, “Diabetes prediction using logistic regression and K-nearest neighbor,” in Cong. Intell. Syst. Lect. Notes Data Eng. Commun. Technol., vol 111. Springer, Singapore, 2022, doi: 10.1007/978-981-16-9113-3_30.
[14] M.J. Sai et al., “An ensemble of light gradient boosting machine and adaptive boosting for prediction of type-2 diabetes,” Int. J. Comput. Intell. Syst., vol. 16, no. 1, 2023, doi: 10.1007/s44196-023-00184-y.
[15] A. Mahabub, “A robust voting approach for diabetes prediction using traditional machine learning techniques,” SN Appl. Sci., vol. 1, no. 12, 2019, doi: 10.1007/s42452-019-1759-7.
[16] Z. Mushtaq et al., “Voting classification-based diabetes mellitus prediction using hypertuned machine-learning techniques,” Mobile Inf. Syst., vol. 2022, pp. 1-16, 2022, doi: 10.1155/2022/6521532.
[17] UCI Machine Learning, “Pima Indians diabetes database,” 2016, [Online]. Available: https://www.kaggle.com/uciml/pima-indians-diabetes-database
[18] R. Muthukrishnan and R. Rohini, “LASSO: A feature selection technique in predictive modeling for machine learning,” in Proc. IEEE Int. Conf. Adv. Comput. App. (ICACA), Coimbatore, India, 2016, pp. 18-20, doi: 10.1109/ICACA.2016.7887916.
[19] H. Veisi, H.R. Ghaedsharaf, and M. Ebrahimi, “Improving the Performance of Machine Learning Algorithms for Heart Disease Diagnosis by Optimizing Data and Features,” Soft Comput. J., vol. 8, no. 1, pp. 70-85, 2019, doi: 10.22052/8.1.70 [In Persian].
[20] F. Leon, S.-A. Floria, and C. Badica, “Evaluating the effect of voting methods on ensemble-based classification,” in Proc. Int. Conf. Innovat. Intell. Syst. App. (INISTA), Gdynia, Poland, 2017, pp. 1-6, doi: 10.1109/INISTA.2017.8001122.
[21] R. Taimourei-Yansary, M. Mirzarezaee, M. Sadeghi, and B. Nadjar Araabi, “Predicting invasive disease-free survival time in breast cancer patients using semi-supervised graph-based machine learning techniques,” Soft Comput. J., vol. 10, no. 1, pp. 48-69, 2021, doi: 10.22052/scj.2022.243330.1039 [In Persian].
[22] R. Akhoondi and R. Hosseini, “A Novel Fuzzy-Genetic Differential Evolutionary Algorithm for Optimization of A Fuzzy Expert Systems Applied to Heart Disease Prediction,” Soft Comput. J., vol. 6, no. 2, pp. 32-47, dor: 20.1001.1.23223707.1396.6.2.3.7 [In Persian].
[23] R. Rastogi and M. Bansal, “Diabetes prediction model using data mining techniques,” Measurement: Sensors, vol. 24, p. 100605, 2022, doi: 10.1016/j.measen.2022.100605.
[24] G. Battineni, G.G. Sagaro, C. Nalini, F. Amenta, and S.K. Tayebati, “Comparative machine-learning approach: A follow-up study on type 2 diabetes predictions by cross-validation methods,” Machines, vol. 7, no. 4, p. 74, 2019, doi: 10.3390/machines7040074.
[25] D. Choubey, P. Kumar, S. Tripathi, and S. Kumar, “Performance evaluation of classification methods with PCA and PSO for diabetes,” Netw. Model. Anal. Health Informat. Bioinformat., vol. 9, no. 1, 2020, doi: 10.1007/s13721-019-0210-8.
[26] V. Chang, J. Bailey, Q.A. Xu, and Z. Sun, “Pima Indians diabetes mellitus classification based on machine learning (ML) algorithms,” Neural Comput. App., vol. 35, pp. 16157-16173, 2023, doi: 10.1007/s00521-022-07049-z.
[27] S. Kumari, D. Kumar, and M. Mittal, “An ensemble approach for classification and prediction of diabetes mellitus using soft voting classifier,” Int. J. Cogn. Comput. Eng., vol. 2, pp. 40-46. 2021.
[28] P. Houngue and A. G. Bigirimana, “Leveraging pima dataset to diabetes prediction: Case study of deep neural network,” J. Comput. Commun., vol. 10, no. 11, pp. 15-28, 2022, doi: 10.4236/jcc.2022.1011002.