Research on Mental Health Prediction using ML

Abstract

This review paper explores the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques in mental health detection and therapy. From early diagnosis to personalized interventions, AI has introduced scalable, accessible solutions for mental health challenges. By investigating existing strategies, such as facial expression investigation and chatbots, and tending to challenge like moral concerns and information quality, this paper emphasizes AI\'s transformative part in mental wellbeing care.

Introduction

Mental health disorders are a growing global issue, affecting all socioeconomic groups, with depression expected to become a leading cause of disease burden. However, there is a shortage of qualified mental health professionals, complicating diagnosis, which is already challenging due to overlapping symptoms and complexity, especially in children.

Artificial Intelligence (AI), particularly Machine Learning (ML), is emerging as a powerful tool to aid in mental health diagnosis by analyzing clinical and behavioral data to improve accuracy and efficiency. Various ML techniques—such as decision trees, neural networks, fuzzy logic, and support vector machines—have been explored since the 1980s to diagnose conditions like anxiety, ADHD, depression, and schizophrenia with varying success.

The methodology involves collecting comprehensive datasets (demographics, symptoms, assessments), preprocessing data (feature selection, normalization, handling missing data, class balancing), training multiple ML models (MLP, Random Forest, Logistic Regression, SVM), and evaluating them based on accuracy, precision, recall, and ROC-AUC. The best model is deployed via a web-based system for real-time detection with continuous improvements enabled by ongoing data and expert feedback.

Conclusion

AI-powered frameworks hold monstrous guarantees for changing mental wellbeing care by advertising convenient conclusion, versatile treatment apparatuses, and personalized mediations. Facial acknowledgment innovations, chatbots, and prescient models have as of now appeared empowering comes about in identifying and tending to mental wellbeing challenges. In any case, for broad appropriation, it is crucial to address moral concerns, guarantee vigorous information quality, and create models that are comprehensive and versatile to different populaces. Long-term mental health care lies at the crossing point of innovation and sympathy, where AI frameworks help instead of supplanting human experts, guaranteeing that mental wellbeing bolster is both available and viable

References

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Copyright

Copyright © 2025 Ms. Jaishree Wankhede , Shriram Phatale, Pranjay Dharkar, Rohan Ramteke, Ishika Rawlani, Dimple Mathare. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.