Predicting restriction of life-space mobility: a machine learning analysis of the IMIAS study.

Manuel Pérez-Trujillo Carmen-Lucía Curcio Néstor Duque-MéndezAlejandra DelgadoLaura CanoJosé Fernando Gómez

Published in: Aging clinical and experimental research (2022)

The model identified risk factors through ML algorithms that could be used to predict LSM restriction; these risk factors could be used by practitioners to identify older adults with an increased risk of LSM reduction in the future. The XGBoost model offers benefits as a complementary method of traditional statistical approaches to understand the complexity of mobility.

Keyphrases

risk factors
machine learning
primary care
deep learning
artificial intelligence
current status
general practice
community dwelling