Accurate Prediction and Detection of Suicidal Risk using Random Forest Algorithm

This research study intends to create a reliable suicide risk assessment system by applying Machine Learning (ML) methods, in particular Random Forest algorithms is being compared with Support Vector Machines (SVM) for achieving the better accuracy results. The goal is to develop a trustworthy tool that can reliably identify individuals that are at risk of self-harm by analyzing their clinical, behavioral, and demographic data. Materials and Methods: The present work has involved two groups. Group 1 refers to the study intended based on Machine Learning Algorithm in which Support Vector Machine (SVM) has been implemented and has secured lower accuracy rates. Group 2 refers the method uses Random Forest to accurately assess the risk of suicide. It makes use of preprocessed health data to enable effective intervention and research advancement. The proposed method performs well in precise suicide prevention analysis with high metrics such as accuracy 0.85, precision 0.85, recall 0.85, and F1 score 0.85, making it useful for prevention analysis technique. The study concludes by showing the effectiveness of Random Forest in preventing suicide and emphasizing how the data-driven methods and user-friendly interfaces may be applied in the real world to improve mental health.