Suicidal Ratio Prediction Among the Continent of World: A Machine Learning Approach

Abstract

Suicide is a global health issue with significant negative effects. Individuals at risk of suicide often avoid seeking help due to stigma or fear of forced treatment, and those with mental illnesses, who make up the majority of suicide victims, may not be aware of their condition or risk. Detecting those at risk of suicide is a challenge for healthcare providers. However, advances in artificial intelligence (AI) may lead to the development of new suicide prediction technologies. This study used machine learning to predict suicide rates across different continents using six common classification algorithms: Stochastic Gradient Descent Classifier (SGDC), Random Forest Classifier (RFC), Gaussian Naive Bayes Classifier (GNBC), K-Neighbors Classifier (KNNC), Logistic Regression Classifier (LRC), and Linear Support Vector Classifier (LSVC). The KNNC algorithm had the highest training accuracy at 100%, and a 97% test accuracy. The RFC algorithm achieved the highest test accuracy at 99%, with a corresponding training accuracy of 99%.