PERTANIKA JOURNAL OF TROPICAL AGRICULTURAL SCIENCE

 

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Pertanika Journal of Tropical Agricultural Science, Volume J, Issue J, January J

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  • Abayadeera, N., Jayawardena, R., & Byrne, N. M. (2019). Machine learning-based models for diabetes risk prediction in urban Filipinos. Journal of Diabetes Research, 2019, 1-8. https://doi.org/10.1155/2019/3709346

  • Biau, G. (2012). Analysis of a random forests model. Journal of Machine Learning Research, 13(1), 1063-1095.

  • Breiman, L., Friedman, J., Stone, C. J., & Olshen, R. A. (1984). Classification and regression trees. CRC press.

  • Chen, T., Li, Z. H., Yuan, C. X., & Wong, K. C. (2004, July 4-8). Improving bagging algorithms: Anti-overfitting by bagging adaptive boosting. [Paper presentation]. Proceedings of the Twenty-first International Conference on Machine Learning (ICML), Alberta, Canada.

  • Han, J., & Kamber, M. (2001). Data mining: Concepts and techniques. Morgan Kaufmann Publishers.

  • International Diabetes Federation. (2019). IDF diabetes atlas (9th ed.). International Diabetes Federation. https://www.diabetesatlas.org/upload/resources/material/20200302_133351_IDFATLAS9e-final-web.pdf

  • Jia, Q., Chen, F., Wang, Y., Huang, B., & Chen, Y. (2018). Application of bagged decision trees for predicting diabetes mellitus in urban Chinese residents. Journal of Healthcare Engineering, 2018, 1-10.

  • Mujumdar, A., & Vaidehi, V. (2019). Diabetes prediction using machine learning algorithms. Procedia Computer Science, 165, 292-299. https://doi.org/10.1016/j.procs.2020.01.047

  • Nguyen, T. T., Tran, T. H., & Nguyen, H. H. (2020, November 12-14). Feature selection techniques for diabetes prediction. [Paper presentation]. Proceedings of the International Conference on Advanced Data Mining and Applications, Foshan, China.

  • Nishat, M. M., Faisal, F., Mahbub, M. A., Mahbub, M. H., Islam, S., & Hoque, M. A. (2021). Performance assessment of different machine learning algorithms in predicting diabetes mellitus. Bioscience Biotechnology Research Communications, 14(1), 74-82. https://doi.org/10.21786/bbrc/14.1/10

  • Pang, B., Wang, C., Lu, Y., Cao, J., Zhang, Y., & Jing, L. (2017). Predicting the risk of diabetes mellitus using machine learning techniques. Frontiers in Genetics, 8, 1-8.

  • Philippine Statistics Authority. (2020). 2019 National nutrition survey final results. Philippine Statistics Authority. https://psa.gov.ph/nutrition-statistics/2019NNSTables

  • Quinlan, J. R. (1993). C4.5: Programs for machine learning. Morgan Kaufmann Publishers.

  • Tan, C., Ona, E. T., Yu, W., Garabiles, M. R., & Sy, R. G. (2019). Predictive modeling of type 2 diabetes risk among Filipinos using decision trees and logistic regression. Diabetes Research and Clinical Practice, 153, 177-185.

  • Wang, Y., Cao, Y., & Zhang, Y. (2018). An adaptive bagging algorithm for imbalanced data classification. Applied Soft Computing, 71, 1018-1030.

  • Zhang, Y., Liu, L., & Li, Q. (2018, July 16-20). A feature selection method based on PSO-SVM for diabetes prediction. [Paper presentation]. IEEE International Conference on Software Quality, Reliability and Security Companion, Lisbon, Portugal.

  • Zhao, Y., Feng, X., Li, L., Liu, Y., & Zhang, X. (2019). Prediction of diabetes using support vector machine algorithm based on medical examination data. BMC Medical Informatics and Decision Making, 19(2), 1-9.

ISSN 1511-3701

e-ISSN 2231-8542

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