Prediction of suicidal risk using machine learning models
Kashyap GS, Siddiqui A, Siddiqui R, Malik K, Wazir S, Brownlee AE (2024)
Publication Language: English
Publication Type: Book chapter / Article in edited volumes
Publication year: 2024
Publisher: CRC Press
Edited Volumes: Research Advances in Intelligent Computing. Volume 2
City/Town: Boca Raton
Pages Range: 167-186
ISBN: 9781040010433
Abstract
According to WHO (World Health Organization), every 40 seconds a person dies of suicide. This amounts to a total of 800,000 people every year falling victim to suicides. Suicide is a global phenomenon: it accounts for 1.4% of all deaths worldwide and costs about $51 billion annually to the healthcare industry. Targeted and timely interventions are critical to helping the patients who are dealing with suicidal symptoms. Data availability is high in the healthcare industry, and this can be used to extract knowledge for better prognosis, diagnosis, treatment, and drug development. In this chapter, we have focused on predicting suicidal risk by using various types of machine learning models. The highest accuracy among our predictive machine learning models is 98.8% test accuracy and 96.3% tenfold cross-validation accuracy using the XGBoost model, which is good compared to existing models present in the literature.
Involved external institutions
Jamia Hamdard
India (IN)
Arizona State University (ASU)
United States (USA) (US)
University of Stirling
United Kingdom (GB)
India (IN)
Arizona State University (ASU)
United States (USA) (US)
University of Stirling
United Kingdom (GB)
How to cite
APA:
Kashyap, G.S., Siddiqui, A., Siddiqui, R., Malik, K., Wazir, S., & Brownlee, A.E. (2024). Prediction of suicidal risk using machine learning models. In Anshul Verma, Pradeepika Verma (Eds.), Research Advances in Intelligent Computing. Volume 2. (pp. 167-186). Boca Raton: CRC Press.
MLA:
Kashyap, Gautam Siddharth, et al. "Prediction of suicidal risk using machine learning models." Research Advances in Intelligent Computing. Volume 2. Ed. Anshul Verma, Pradeepika Verma, Boca Raton: CRC Press, 2024. 167-186.
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