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August 17, 2025

Predictive Analysis of Mental Health Using Machine Learning for Depression Prediction

Key Points

Achieved an accuracy of 92% with random forest, demonstrating effective classification for early depression prediction.
The study reported a 0.99 F1 score using Linear SVM, highlighting high predictive performance among tested algorithms.
Utilizing various machine learning algorithms including decision trees and voting classifiers for improved detection accuracy.
This work indicates that machine learning could enhance timely depression detection and intervention strategies.

Abstract

This work aims to predict depression based on diverse data using Machine Learning Algorithms. The designed model seeks to identify early indicators of depression, providing a potential tool for proactive intervention and support in mental health by analyzing patterns in behavioral, physiological, and contextual data. Machine learning algorithms, namely decision trees, extra trees, XGBoost, Stochastic gradient descent, grid search CV, Stacking, and Voting classifiers, etc., are used to predict depression in the early stage. This study emphasizes integrating machine learning techniques to enhance predictive accuracy and contribute to developing accessible and timely depression detection systems. The F1 score was added, which helped to identify the best machine learning algorithm among the ones applied. We have achieved an accuracy of 92 % with random forest, which is 3% higher than the work previously done in RF. We also achieved a 0.99 F1 score using Linear SVM.

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