Data-driven suicide risk prediction in patients suffering from chronic diseases using machine learning.

Abstract

Suicide is a critical public health issue worldwide, influenced by environmental factors such as economic stress and limited social support, as well as individual risk factors. Patients with chronic health conditions may face heightened vulnerability due to overlapping psychological and medical challenges. This research explores the application of Machine Learning (ML) techniques to identify suicide risk among such patients, utilizing data from the National Health and Nutrition Examination Survey (NHANES). The study incorporated demographic, clinical, and psycho-social variables, including depression, substance use, hypertension, and diabetes, to develop predictive models. Several ML algorithms were trained and evaluated using standard performance metrics to assess predictive accuracy. Among the models, Gradient Boosting Machine (GBM) achieved the strongest performance, with a receiver operating characteristic area under the curve (ROC-AUC) of 0.9479. Random Forest also performed exceptionally, with a ROC-AUC of 0.9301, while four additional models showed competitive results. These algorithms effectively captured complex nonlinear relationships and interactions between multiple risk factors, demonstrating their suitability for multivariable health data. The findings underscore the potential of integrating ML into Electronic Medical Records (EMRs) as decision-support tools to identify high-risk patients. Early detection enables timely interventions, which may significantly improve mental health outcomes and reduce suicide risk.