Machine Learning for Suicide Risk Assessment on Depressed Patients

Abstract

Suicide is a global phenomena and the leading cause of death in some countries and age groups, accounting for nearly 1 million deaths and 10 million attempts per year. It costs society a substantial amount of money both directly and indirectly not to mention the tremendous amount of emotional distress and pain for families and friends. This thesis is the first of its kind that tries to automate a manual process of suicide risk assessment with machine learning techniques, successfully doing so with support vector machine models. A precision of 82% and an accuracy of 89% is reached and proves the potential to further develop this method for assessing suicide risk among depressed patients.