Suicide Rate Prediction Using Machine Learning Approach

Abstract

Suicide, a complex public health concern, requires innovative approaches toprevention. This study utilizes advanced machine learning techniques to forecast suicide rates, employing a diverse dataset covering demographics, socio-economics, mental health, and social media behaviour. Decision trees, support vector machines, and neural networks are employed for their ability to discern patterns within large datasets. Feature engineering extracts relevant information, integrating demographic, economic, mental health, and social factors. Model performance is evaluated using precision, recall, and F1 score, with cross-validation and hyperparameter tuning to ensure generalizability and accuracy. Ethical considerations include anonymization, privacy safeguards, and bias mitigation strategies. Transparent models foster trust and understanding among stakeholders. Practical insights aim to inform policymakers, mental health professionals, and public health practitioners, enabling targeted interventions for high-risk populations. By leveraging predictive analytics, resources can be allocated more efficiently, enhancing the impact of suicide prevention strategies. Overall, this research pioneers a comprehensive approach to suicide rate prediction, contributing to evidence-based prevention strategies and ultimately reducing the global burden of suicide.