A Hybrid Ensemble Learning Approach Utilizing Light Gradient Boosting Machine and Category Boosting Model for Lifestyle-Based Prediction of Type-II Diabetes Mellitus

Author:

Nagassou Mahadi,Mwangi Ronald Waweru,Nyarige Euna

Publisher

Scientific Research Publishing, Inc.

Subject

General Earth and Planetary Sciences,General Environmental Science

Reference51 articles.

1. 5. Facilitating Behavior Change and Well-being to Improve Health Outcomes: Standards of Medical Care in Diabetes—2021

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3. Ke, G.L., Meng, Q., Finley, T., Wang, T.F., Chen, W., Ma, W.D., Ye, Q.W. and Liu, T.Y. (2017) LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, 4-9 December 2017, 3149-3157.

4. Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V. and Gulin, A. (2018) Catboost: Unbiased Boosting with Categorical Features. Proceedings of the 32nd International Conference on Neural Information Processing Systems, Montréal, 3-8 December 2018, 6639-6649.

5. Snoek, J., Larochelle, H. and Adams, R.P. (2012) Practical Bayesian Optimization of Machine Learning Algorithms. Proceedings of the 25th International Conference on Neural Information Processing Systems, Nevada, 3-6 December 2012, 2951-2959.

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