RT Journal Article
T1 Comparing and Tuning Machine Learning Algorithms to Predict Type 2 Diabetes Mellitus
A1 Aguilera-Venegas, Gabriel
A1 López-Molina, Amador
A1 Rojo-Martínez, Gemma
A1 Galán-García, José Luis
K1 Diabetes
K1 Redes neuronales (Informática)
K1 Inteligencia artificial
AB 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, RandomForest, 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.
PB Elsevier
YR 2023
FD 2023
LK https://hdl.handle.net/10630/26536
UL https://hdl.handle.net/10630/26536
LA eng
NO 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
NO This work was partially supported by the Ministerio de Sanidad, Servicios Sociales e Igualdad-ISCIII, Instituto de SaludCarlos III (PI20/01322), European Regional Development Fund (ERDF) ‘‘A way to build Europe’’. Funding for open accesscharge: Universidad de Málaga/CBUA. We thank the anonymous reviewers for their useful suggestions and correctionswhich have improved the quality of the paper.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026