Suicide Rate Prediction Using Machine Learning

Suicide is a growing public health concern with accurate prediction and prevention being crucial for saving lives. This project utilized advanced machine learning algorithms to predict suicide rates using a dataset of demographic, economic, mental-health and social factors. Our approach employs a combination of regression models, decision trees, Random Forest, multilayered perception to identify key predictors and patterns in suicide rates. Results show that our machine learning model achieves a high accuracy rate in predicting suicide rates, outperforming traditional statistical methods. Feature importance analysis reveals that economic factors, such as unemployment rates and income levels, are significant predictors of suicide rates. These findings have important implications for policy makers and mental health professionals, enabling targeted interventions and resource allocation to high-risk populations. Our study demonstrates the potential of machine learning in improving suicide prevention of machine learning in improving suicide prevention efforts and highlights the need for further research in this critical area.