Depression prediction using machine learning: a review

Predicting depression can mitigate tragedies. Numerous works have been proposed so far using machine learning algorithms. This paper reviews publications from online electronic databases 2016 to 2020 that use techniques predict depression. The aim of this study is identify important variables used in prediction, recent screening tools adopted, and the latest algorithms used. understanding provides researchers with fundamental components essential Fifteen articles were found relevant. We based our review on systematic mapping (SMS) method. Three research questions answered through review. discovered sixteen deemed by literature. Not all reviewed literature utilizes prediction process. Nevertheless, five discovered, most frequently hospital anxiety scale (HADS) hamilton rating (HDRS) for general population, while targeting older population geriatric (GDS) was often employed. A total twenty-two identified employed random forest be reliable algorithm across publications.