PERTANIKA JOURNAL OF TROPICAL AGRICULTURAL SCIENCE

J

• Agrawal, K., Bhargav, G., & Spandana, E. (2021). Diabetes diagnosis prediction using ensemble approach. In V. Nath & J. K. Mandal (Eds.), Proceedings of the Fourth International Conference on Microelectronics, Computing and Communication Systems: Lecture Notes in Electrical Engineering, vol 673 (pp. 799-813). Springer. https://doi.org/10.1007/978-981-15-5546-6_66

• Agresti, A. (2015). Foundations of linear and generalized linear models. John Wiley & Sons

• Akyol, K., & Şen, B. (2018). Diabetes mellitus data classification by cascading of feature selection methods and ensemble learning algorithms. International Journal of Modern Education & Computer Science, 10(6), 10-16. https://doi.org/10.5815/ijmecs.2018.06.02

• Alasaady, M. T., Aris, T. N. M., Sharef, N. M., & Hamdan, H. (2022). A proposed approach for diabetes diagnosis using neuro-fuzzy technique. Bulletin of Electrical Engineering and Informatics, 11(6), 3590–3597. https://doi.org/10.11591/eei.v11i6.4269

• Alasaady, M. T., Saeed, M. G., & Faraj, K. H. (2019, February 13-14). Evaluation and comparison framework for data modeling languages. [Paper presentation]. 2nd International Conference on Electrical, Communication, Computer, Power and Control Engineering (ICECCPCE), Mosul, Iraq. https://doi.org/10.1109/ICECCPCE46549.2019.203750

• Atif, M., Anwer, F., & Talib, F. (2022). An ensemble learning approach for effective prediction of diabetes mellitus using hard voting classifier. Indian Journal of Science and Technology, 15(39), 1978–1986. https://doi.org/10.17485/IJST/v15i39.1520

• Barik, S., Mohanty, S., Mohanty, S., & Singh, D. (2021). Analysis of prediction accuracy of diabetes using classifier and hybrid machine learning techniques. In D. Mishra, R. Buyya, P. Mohapatra & S. Patnaik (Eds.), Intelligent and Cloud Computing (pp. 399–409). Springer. https://doi.org/10.1007/978-981-15-6202-0_41

• Berner, R., & Judge, K. (2019). The Data Standardization Challenge (Working Paper No. 438/2019). CIGI Press. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3323719

• Breiman, L. (1996). Bagging predictors. Machine Learning, 24, 123-140. https://doi.org/10.1007/BF00058655.

• Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324

• Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (2017). Classification and Regression Trees. Routledge. https://doi.org/10.1201/9781315139470

• Breunig, M. M., Kriegel, H. P., Ng, R. T., & Sander, J. (2000, May 15-18). LOF: Identifying density-based local outliers. [Paper presentation] SIGMOD ‘00: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, Texas, USA. https://doi.org/10.1145/342009.335388

• Buuren, S. V. (2012). Flexible imputation of missing data. CRC Press. https://doi.org/10.1201/b11826

• Caruana, R., Niculescu-Mizil, A., Crew, G., & Ksikes, A. (2004, July 4-8). Ensemble selection from libraries of models. [Paper presentation]. ICML ‘04: Proceedings of the Twenty-first International Conference on Machine Learning, New York, USA. https://doi.org/10.1145/1015330.1015432

• Centers for Disease Control and Prevention (2011). National diabetes fact sheet: National estimates and general information on diabetes and prediabetes in the United States. Atlanta, GA: US Department of Health and Human Services, Centers for Disease Control and Prevention, 201(1), 2568–2569.

• Chen, R., Ovbiagele, B., & Feng, W. (2016). Diabetes and stroke: Epidemiology, pathophysiology, pharmaceuticals and outcomes. American Journal of the Medical Sciences, 351(4), 380–386. https://doi.org/10.1016/j.amjms.2016.01.011

• Chen, T., & Guestrin, C. (2016, August 13-17). XGBoost: A scalable tree boosting system. [Paper presentation]. KDD ‘16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, California, USA. https://doi.org/10.1145/2939672.2939785

• Cover, T., & Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21–27. https://doi.org/10.1109/TIT.1967.1053964

• El Houby, E. M. F., Yassin, N. I. R., & Omran, S. (2017). A hybrid approach from ant colony optimization and K-nearest neighbor for classifying datasets using selected features. Informatica, 41, 495–506.

• Fernández-Delgado, M., Cernadas, E., Barro, S., & Amorim, D. (2014). Do we need hundreds of classifiers to solve real-world classification problems? The Journal of Machine Learning Research, 15(1), 3133–3181.

• Ganesh, P. V. S., & Sripriya, P. (2020). A comparative review of prediction methods for pima indians diabetes dataset. In S. Smys, J. M. R. S. Tavares, V. E. Balas & A. M. Iliyasu (Eds.), Computational Vision and Bio-Inspired Computing (pp. 735–750). Springer. https://doi.org/10.1007/978-3-030-37218-7_83

• Gelman, A., & Hill, J. (2006). Data Analysis using Regression and Multilevel/Hierarchical Models. Cambridge University Press. https://doi.org/10.1017/CBO9780511790942

• Han, J., Pei, J., & Tong, H. (2022). Data mining: Concepts and techniques. Morgan Kaufmann.

• Hosmer, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied logistic regression (3rd ed.). Wiley. https://doi.org/10.1002/9781118548387

• Kavakiotis, I., Tsave, O., Salifoglou, A., Maglaveras, N., Vlahavas, I., & Chouvarda, I. (2017). Machine learning and data mining methods in diabetes research. Computational and Structural Biotechnology Journal, 15, 104–116. https://doi.org/10.1016/j.csbj.2016.12.005

• Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T. Y. (2017). LightGBM: A highly efficient gradient boosting decision tree. In I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan & R. Garnett (Eds.), Advances in Neural Information Processing Systems (pp. 30). Curran Associates, Inc.

• Khairan, H. E., Zubaidi, S. L., Muhsen, Y. R., & Al-Ansari, N. (2023). Parameter optimisation-based hybrid reference evapotranspiration prediction models: A systematic review of current implementations and future research directions. Atmosphere, 14(1), Article 77. https://doi.org/10.3390/atmos14010077

• Kumari, S., Kumar, D., & Mittal, M. (2021). An ensemble approach for classification and prediction of diabetes mellitus using soft voting classifier. International Journal of Cognitive Computing in Engineering, 2, 40–46. https://doi.org/10.1016/j.ijcce.2021.01.001

• Kunwar, R., & Timalsina, A. K. (2021). An ensemble approach for the diagnosis of diabetes mellitus using multiple classifiers. Proceedings of 9th IOE Graduate Conference, 9, 202-207.

• Li, L. (2014, November 10-12). Diagnosis of diabetes using a weight-adjusted voting approach. [Paper presentation]. IEEE International Conference on Bioinformatics and Bioengineering, Florida, USA. https://doi.org/10.1109/BIBE.2014.27

• Mahabub, A. (2019). A robust voting approach for diabetes prediction using traditional machine learning techniques. SN Applied Sciences, 1(12), Article 1667. https://doi.org/10.1007/s42452-019-1759-7

• Mansour, Y., & Schain, M. (2001). Learning with maximum-entropy distributions. Machine Learning, 45(2), 123–145. https://doi.org/10.1023/A:1010950718922

• Mirzajani, S. S., & Salimi, S. (2018). Prediction and diagnosis of diabetes by using data mining techniques. Avicenna Journal of Medical Biochemistry, 6(1), 3–7. https://doi.org/10.15171/ajmb.2018.02

• Noor, N. A. B. S., Elamvazuthi, I., & Yahya, N. (2021, July 13-15). Classification of diabetes mellitus using ensemble algorithms. [Paper presentation]. 8th International Conference on Intelligent and Advanced Systems (ICIAS), Kuching, Sarawak. https://doi.org/10.1109/ICIAS49414.2021.9642508

• Prema, N. S., Varshith, V., & Yogeswar, J. (2019). Prediction of diabetes using ensemble techniques. International Journal of Recent Technology and Engineering, 7(6), 203-205.

• Qin, L. (2022, September 23-25). A prediction model of diabetes based on ensemble learning. [Paper presentation] AIPR ‘22: Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition, Xiamen China. https://doi.org/10.1145/3573942.3573949

• Saeed, R. R., Yaseen, O. M., Rashid, M. M., & Ahmed, M. R. (2022, June 9-11). Applications of machine learning in battling against novel COVID-19. [Paper presentation]. International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), Ankara, Turkey. https://doi.org/10.1109/HORA55278.2022.9799969

• Shanker, M., Hu, M. Y., & Hung, M. S. (1996). Effect of data standardization on neural network training. Omega, 24(4), 385–397. https://doi.org/10.1016/0305-0483(96)00010-2

• Singh, N., & Singh, P. (2020). Stacking-based multi-objective evolutionary ensemble framework for prediction of diabetes mellitus. Biocybernetics and Biomedical Engineering, 40(1), 1–22. https://doi.org/10.1016/j.bbe.2019.10.001

• Soni, M., & Varma, S. (2020). Diabetes prediction using machine learning techniques. International Journal of Engineering Research & Technology, 9(9), 921-925.

• Swapna, G., Soman, K. P., & Vinayakumar, R. (2018). Automated detection of diabetes using CNN and CNN-LSTM network and heart rate signals. Procedia Computer Science, 132, 1253–1262. https://doi.org/10.1016/j.procs.2018.05.041

• WHO. (2014). World diabetes statistics. World Health Organization. http://www.who.int/diabetes/en/index.html