Performance of Artificial Intelligence in Predicting Future Depression Levels

Depression is a prevalent mental condition that is challenging to diagnose using conventional techniques. Using machine learning and deep learning models with motor activity data, wearable AI technology has shown promise in reliably and effectively identifying or predicting depression. In this work, we aim to examine the performance of simple linear and non-linear models in the prediction of depression levels. We compared eight linear and non-linear models (Ridge, ElasticNet, Lasso, Random Forest, Gradient boosting, Decision trees, Support vector machines, and Multilayer perceptron) for the task of predicting depression scores over a period using physiological features, motor activity data, and MADRAS scores. For the experimental evaluation, we used the Depresjon dataset which contains the motor activity data of depressed and non-depressed participants. According to our findings, simple linear and non-linear models may effectively estimate depression scores for depressed people without the need for complex models. This opens the door for the development of more effective and impartial techniques for identifying depression and treating/preventing it using commonly used, widely accessible wearable technology.