Predicting suicidal behavior among Indian adults using childhood trauma,
  mental health questionnaires and machine learning cascade ensembles
Akash K Rao Gunjan Y Trivedi Riri G Trivedi Anshika Bajpai Gajraj Singh Chauhan Vishnu K Menon Kathirvel Soundappan Hemalatha Ramani Neha Pandya Varun Dutt
Among young adults, suicide is India's leading cause of death, accounting for
an alarming national suicide rate of around 16%. In recent years, machine
learning algorithms have emerged to predict suicidal behavior using various
behavioral traits. But to date, the efficacy of machine learning algorithms in
predicting suicidal behavior in the Indian context has not been explored in
literature. In this study, different machine learning algorithms and ensembles
were developed to predict suicide behavior based on childhood trauma, different
mental health parameters, and other behavioral factors. The dataset was
acquired from 391 individuals from a wellness center in India. Information
regarding their childhood trauma, psychological wellness, and other mental
health issues was acquired through standardized questionnaires. Results
revealed that cascade ensemble learning methods using a support vector machine,
decision trees, and random forest were able to classify suicidal behavior with
an accuracy of 95.04% using data from childhood trauma and mental health
questionnaires. The study highlights the potential of using these machine
learning ensembles to identify individuals with suicidal tendencies so that
targeted interinterventions could be provided efficiently.