TY - GEN
T1 - Predicting Suicidal Behavior Among Indian Adults Using Childhood Trauma, Mental Health Questionnaires and Machine Learning Cascade Ensembles
AU - Rao, Akash K.
AU - Trivedi, Gunjan Y.
AU - Trivedi, Riri G.
AU - Bajpai, Anshika
AU - Chauhan, Gajraj Singh
AU - Menon, Vishnu K.
AU - Soundappan, Kathirvel
AU - Ramani, Hemalatha
AU - Pandya, Neha
AU - Dutt, Varun
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
PY - 2024
Y1 - 2024
N2 - 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 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 interventions could be provided efficiently.
AB - 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 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 interventions could be provided efficiently.
UR - https://www.scopus.com/pages/publications/85199523989
UR - https://www.scopus.com/inward/citedby.url?scp=85199523989&partnerID=8YFLogxK
U2 - 10.1007/978-981-97-2611-0_17
DO - 10.1007/978-981-97-2611-0_17
M3 - Conference contribution
AN - SCOPUS:85199523989
SN - 9789819726103
T3 - Lecture Notes in Networks and Systems
SP - 247
EP - 257
BT - Proceedings of 4th International Conference on Frontiers in Computing and Systems - COMSYS 2023
A2 - Kole, Dipak Kumar
A2 - Roy Chowdhury, Shubhajit
A2 - Basu, Subhadip
A2 - Plewczynski, Dariusz
A2 - Bhattacharjee, Debotosh
PB - Springer Science and Business Media Deutschland GmbH
T2 - 4th International Conference on Frontiers in Computing and Systems, COMSYS 2023
Y2 - 16 October 2023 through 17 October 2023
ER -
