In Diagnostics (Basel, Switzerland)
Major Depression Disease has been increasing in the last few years, affecting around 7 percent of the world population, but nowadays techniques to diagnose it are outdated and inefficient. Motor activity data in the last decade is presented as a better way to diagnose, treat and monitor patients suffering from this illness, this is achieved through the use of machine learning algorithms. Disturbances in the circadian rhythm of mental illness patients increase the effectiveness of the data mining process. In this paper, a comparison of motor activity data from the night, day and full day is carried out through a data mining process using the Random Forest classifier to identified depressive and non-depressive episodes. Data from Depressjon dataset is split into three different subsets and 24 features in time and frequency domain are extracted to select the best model to be used in the classification of depression episodes. The results showed that the best dataset and model to realize the classification of depressive episodes is the night motor activity data with 99.37% of sensitivity and 99.91% of specificity.
Rodríguez-Ruiz Julieta G, Galván-Tejada Carlos E, Zanella-Calzada Laura A, Celaya-Padilla José M, Galván-Tejada Jorge I, Gamboa-Rosales Hamurabi, Luna-García Huizilopoztli, Magallanes-Quintanar Rafael, Soto-Murillo Manuel A
data mining, depression, depressive episodes, motor activity, night, random forest