Context-Aware Suicide Rate Prediction for Significant Mental Health Monitoring in Smart Cities

In this work, suicide trends are studied as an important public health challenge within smart city environments. The research examines contributing factors and uses data analysis, visualization, and regression-based prediction approaches to better understand suicide patterns over time. A combined predictive model is used to estimate total suicide counts based on factors such as state, age group, and year. The results support mental health awareness and highlight the value of data-driven approaches for prevention and monitoring.

In Digital Cities