Feature Selection of Motor Activity in Intervals of Time with Genetics Algorithms for Depression Detection

Carlos H. Espino-Salinas; Carlos E. Galván-Tejada; Ana G. Sánchez-Reyna; Huizilopoztli Luna-García; Hamurabi Gamboa-Rosales; Jorge A. Morgan-Benita; José M. Celaya-Padilla; Jorge I.  Galván-Tejada

doi:10.17488/RMIB.44.4.3

Authors

Carlos H. Espino-Salinas Universidad Autónoma de Zacatecas, México https://orcid.org/0000-0001-8092-1333
Carlos E. Galván-Tejada Universidad Autonoma de Zacatecas, México https://orcid.org/0000-0002-7635-4687
Ana G. Sánchez-Reyna Universidad Autonoma de Zacatecas, México https://orcid.org/0000-0001-5809-532X
Huizilopoztli Luna-García Universidad Autonoma de Zacatecas, México https://orcid.org/0000-0001-5714-7482
Hamurabi Gamboa-Rosales Universidad Autonoma de Zacatecas, México
Jorge A. Morgan-Benita Universidad Autonoma de Zacatecas, México https://orcid.org/0000-0001-7308-6018
José M. Celaya-Padilla Universidad Autonoma de Zacatecas, México https://orcid.org/0000-0001-6847-3777
Jorge I. Galván-Tejada Universidad Autonoma de Zacatecas, México https://orcid.org/0000-0002-7555-5655

DOI:

https://doi.org/10.17488/RMIB.44.4.3

Keywords:

artificial intelligence, depression, feature selection, genetic algorithm, motor activity

Abstract

It is estimated that depression affects more than 300 million people in worldwide. Unfortunately, the current method of psychiatric evaluation requires a great effort on the part of clinicians to collect complete information. The aim of this paper is determine the optimal time intervals to detect depression using genetic algorithms and machine learning techniques; from motor activity readings of 55 participants during a week at one-minute intervals. The time intervals with the best performance in detecting depression in individuals were selected by applying Genetic Algorithms (GA). Methodology. 385 observations of the study participants were evaluated, obtaining an accuracy of 83.0 % with Logistic Regression (LR). Conclusion. There is a relationship between motor activity and people with depression since it is possible to detect it using machine learning techniques. However, the changes in the variables of the time intervals could be established as key factors since, at different times, they could give good or bad results because the motor activity in the patients could vary. However, the results present a first approximation for developing tools that help the opportune and objective diagnosis of depression.

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