2026-05-30T08:08:52Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/245332026-02-03T12:18:45Zcom_10630_2254col_10630_37959

Comparing and Tuning Machine Learning Algorithms to Predict Type 2 Diabetes Mellitus Aguilera-Venegas, Gabriel López-Molina, Amador Galán-García, José Luis Rojo-Martínez, Gemma Diabetes The main goals of this work is to study and compare machine learning algorithms to predict the development of type 2 diabetes mellitus. Four classifi cation algorithms have been considered, studying and comparing the accuracy of each one to predict the incidence of type 2 diabetes mellitus seven years in advance. Specifically, the techniques studied are: Decision Tree, Random Forest, kNN (k-Nearest Neighbors) and Neural Networks. The study not only involves the comparison among these techniques, but also, the tuning of the meta-parameters in each algorithm. The algorithms have been implemented using the language R. The data base used is obtained from the nation-wide cohort di@bet.es study. The conclusions will include the accuracy of each algorithm and therefore the best technique for this problem. The best meta-parameters for each algorithm will be also provided. 2022-07-04T09:32:37Z 2022-07-04T09:32:37Z 2022-06 2022-06-13 conference output https://hdl.handle.net/10630/24533 eng 8th European Seminar on Computing (ESCO 2022) Pilsen (Chequia) 13-06-2022 open access