Developing a Prediction Model Based on Machine Learning Tools and Techniques to Avert Suicide and Suicidal Tendencies of Vulnerable Individuals
Aditi Singh
Department of Psychology, Manipal University, Jaipur
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In today's environment, depression and suicide rates are rising. The most frequent reasons of suicide thoughts in humans are stress, anxiety attacks, depressive illnesses, and other conditions. Elevated psychological strain can set off suicidal thoughts and is the main driving force for suicide attempts. However, the statistics employed in conventional suicide prediction systems take into account the tenuous link between individuals' psychological stress and suicidal ideation. The goal of this research study is to advance the field by developing various machine learning algorithms, such as Random Forest [RF] and Support Vector Machine [SVM], to analyze and predict suicidal ideation in individuals based on the six most significant psychological stress-causing domains and the messages they send to others. Next, an analysis and comparison are conducted between the Random Forest [RF] and SVM methodologies' accuracy. Out of them, the Random Forest has outperformed the Support Vector Machine in terms of accuracy.
Keywords: Machine learning; suicidal tendencies; prediction model
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