Machine Learning based Suicide Prediction

Suicide and depression are on the upswing in today's world. Stress, anxiety attacks, depressive disorders, and other factors are the most common causes of suicidal thoughts in people. Suicidal thoughts can be triggered by high levels of mental stress, which is a primary motivator for suicide attempts. However, in the traditional suicide prediction methods, the statistics are used with the minor association between suicidal thoughts and psychological stress in people. To further advance this field, this research study has developed different ML algorithms like Random Forest [RF] and Support Vector Machine [SVM] to analyze and predict the idea of suicide present in individuals by using the six most important psychological stress-causing domains and the types of messages they send to other people. The accuracy of Random Forest [RF] and SVM methods are then evaluated and compared. Among them, the Random Forest has leveraged better accuracy than the Support Vector Machine.