Machine Learning Approaches to Detect the Preliminary Psychological Vulnerability and Mental Health in Society

Abstract

Mental health disorders represent a significant global challenge, affecting millions of individuals and imposing substantial societal and economic burdens. Traditional approaches to mental health assessment often rely on clinical interviews and subjective evaluations, which may fail to identify early warning signs and preliminary psychological vulnerabilities. This thesis investigates the application of machine learning techniques for the automated detection of psychological vulnerability and mental health conditions in society, with the objective of enabling early intervention and preventive care.
The research employed a comprehensive comparative analysis of multiple machine learning algorithms to evaluate their effectiveness in identifying psychological vulnerabilities. Five distinct models were implemented and assessed: Logistic Regression, K-Nearest Neighbors (KNN), Decision Tree, Random Forest, and a Stacked Model combining multiple algorithmic approaches. The study utilized diverse data sources including physiological signals, behavioral patterns, social media activity, and self-reported measures to create robust predictive frameworks.
Extensive experimentation and validation were conducted to evaluate model performance across key metrics including precision, recall, F1-score, and support measures. The results demonstrated significant variations in algorithmic performance, with traditional approaches such as Logistic Regression achieving precision and recall scores of 0.926 and 0.903 respectively. K-Nearest Neighbors showed comparable performance with precision of 0.926 and recall of 0.906. Decision Tree algorithms exhibited balanced performance across all metrics with precision of 0.923 and recall of 0.926.
The most significant findings emerged from ensemble methods, which demonstrated superior predictive capabilities. Random Forest achieved the highest individual model performance with precision of 0.950, recall of 0.945, and F1-score of 0.950. The Stacked Model, integrating multiple algorithms, showed comparable excellence with precision of 0.950, recall of 0.944, and F1-score of 0.950, confirming the effectiveness of ensemble approaches in psychological vulnerability detection.
The research addresses critical challenges including privacy concerns, algorithmic bias, cultural sensitivity, and the need for extensive validation across diverse populations. Ethical implications of automated mental health screening are thoroughly examined, emphasizing the importance of maintaining transparency in algorithmic decision-making while ensuring these tools enhance rather than replace human clinical judgment.
The findings reveal that machine learning approaches offer unprecedented opportunities for preventive intervention, potentially reducing the societal burden of mental illness through early identification and timely support. The superior performance of ensemble methods, particularly Random Forest and Stacked Models, demonstrates the value of combining multiple algorithmic approaches to achieve robust and reliable predictions.
This thesis contributes to the growing body of knowledge in computational psychiatry and digital mental health, providing evidence for the viability of automated psychological vulnerability detection systems. The research establishes a foundation for future developments in this field while highlighting the importance of interdisciplinary collaboration between technologists, mental health professionals, and ethicists in creating effective, equitable, and responsible mental health screening technologies.
The implications extend beyond technical advancement, offering pathways to democratize access to mental health screening, particularly in underserved communities. However, careful implementation strategies are essential to ensure that technological solutions complement existing support systems while addressing concerns related to the digital divide and potential discrimination.
Keywords: Machine Learning, Mental Health, Psychological Vulnerability, Ensemble Methods, Preventive Healthcare