Machine learning aplicado en la clasificación y predicción de la depresión: Una revisión sistemática

Translated title of the contribution: Statistics applied in the classification and prediction of depression: A systematic review

Research output: Contribution to journalReview articlepeer-review

Abstract

The main challenge for health insurers is to properly manage the disease as well as the health of its members, emphasizing the prevention and implementation of actions that allow the anticipation and prediction of the disease. This article presents a systematic review of the literature on the main machine learning methodologies that allow, through the prediction of mental illnesses, to carry out an early intervention. It was found that the main methodologies for this purpose are statistical models such as logistic regression, Vector support machine and random forest; and that the different indicators of neuroimaging use of cell phonesbecome fundamental predictor variables when it comes to predicting mental illnesses.

Translated title of the contributionStatistics applied in the classification and prediction of depression: A systematic review
Original languageSpanish
Pages (from-to)363-375
Number of pages13
JournalRISTI - Revista Iberica de Sistemas e Tecnologias de Informacao
Issue numberSpecial Issue 47
StatePublished - Jan 2022

Fingerprint

Dive into the research topics of 'Statistics applied in the classification and prediction of depression: A systematic review'. Together they form a unique fingerprint.

Cite this