Construction of a Random Forest-based Machine Learning Model for Depression Prediction: Application to the Analysis of Disordered Behaviors

Abstract

The topics covered in this article include the creation of a bootstrap learning model for depression predictions based on the Random Forest technique. Depression is a severe mental illness that affects millions of people worldwide. This condition causes great pain and affects quality of life, and in the most severe cases, the person takes their own life. Depression has a high incidence, but diagnosis is always complex and often delayed. It is made on the basis of clinical assessment, which is subjective, and questionnaires, which are often inaccurate and cannot identify people at risk early enough because a person's subjective perception can often be distorted. In this context, and to illustrate our point, we aim to show how AI, and more specifically machine learning, can provide innovative applications that can be used to improve early detection cf. prevention of depression risk. Instead of stupidly defining score intervals for a child, we can train a model on a dataset to identify patterns and correlations that escape simple regression analyses. Then, we can anticipate the first signs of log-in with depression, or we can identify which combinations of self and family history are most concerning. To complement our study, we chose the decision tree ensemble algorithm. The article highlights the need for more objective and effective prediction tools for depression, and proposes a machine learning-based solution to achieve this, potentially leading to earlier intervention and better patient care.