The prediction of suicide rate based on machine learning

Suicide rates have a profound impact on individuals and society, making it a significant social issue warranting extensive research. To predict and understand suicide rates, researchers employ various methods, including social science research, psychological surveys, and data analysis. The principal aim of this investigation was to develop a reliable forecasting model that can accurately predict suicide rates and provide credible references for relevant populations. In this research, multiple machine learning and deep learning methods, Neural networks, Random Forests, and Support Vector Machines (SVM) were among the learning techniques used., and XGBoost. These methods were chosen due to their effectiveness in handling complex and multidimensional data. The results demonstrated that a comprehensive analysis of multiple indicators was crucial, with neural networks yielding the best performance and SVM performing the worst. These research findings offer valuable insights into the factors influencing suicide rates and can inform the formulation of prevention and intervention strategies.