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oai:riuma.uma.es:10630/265362026-02-03T11:28:49Zcom_10630_2254col_10630_37953

Comparing and Tuning Machine Learning Algorithms to Predict Type 2 Diabetes Mellitus Aguilera-Venegas, Gabriel López-Molina, Amador Rojo-Martínez, Gemma Galán-García, José Luis Diabetes Redes neuronales (Informática) Inteligencia artificial The main goals of this work are to study and compare machine learning algorithms to predict the development of type 2 diabetes mellitus. Four classification algorithms have been considered, studying and comparing the accuracy of each one to predict the incidence of type 2 diabetes mellitus seven and a half years in advance. Specifically, the techniques studied are: Decision Tree, Random Forest, kNN (k-Nearest Neighbours) and Neural Networks. The study not only involves the comparison among these techniques, but also, the tuning of the hyperparameters of each algorithm. The algorithms have been implemented using the language R. The data base used has been obtained from the nation-wide cohort di@bet.es study. This work includes the accuracy of each algorithm and therefore the best technique for this problem. The best hyperparameters for each algorithm will be also provided. 2023-05-10T07:24:03Z 2023-05-10T07:24:03Z 2023-05-10T07:24:03Z 2023 journal article Aguilera-Venegas, López-Molina, A., Rojo-Martínez, G., & Galán-García, J. L. (2023). Comparing and tuning machine learning algorithms to predict type 2 diabetes mellitus. Journal of Computational and Applied Mathematics, 427. https://doi.org/10.1016/j.cam.2023.115115 https://hdl.handle.net/10630/26536 https://doi.org/10.1016/j.cam.2023.115115 eng http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Attribution-NonCommercial-NoDerivatives 4.0 Internacional Elsevier