RT Conference Proceedings
T1 Comparing and Tuning Machine Learning Algorithms to Predict Type 2 Diabetes Mellitus
A1 Aguilera-Venegas, Gabriel
A1 López-Molina, Amador
A1 Galán-García, José Luis
A1 Rojo-Martínez, Gemma
K1 Diabetes
AB 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.
YR 2022
FD 2022-06-13
LK https://hdl.handle.net/10630/24533
UL https://hdl.handle.net/10630/24533
LA eng
NO Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 21 ene 2026