Analysis and evaluation of explainable artificial intelligence on suicide risk assessment.

Journal: Scientific reports
PMID: 38485985

Abstract

This study explores the effectiveness of Explainable Artificial Intelligence (XAI) for predicting suicide risk from medical tabular data. Given the common challenge of limited datasets in health-related Machine Learning (ML) applications, we use data augmentation in tandem with ML to enhance the identification of individuals at high risk of suicide. We use SHapley Additive exPlanations (SHAP) for XAI and traditional correlation analysis to rank feature importance, pinpointing primary factors influencing suicide risk and preventive measures. Experimental results show the Random Forest (RF) model is excelling in accuracy, F1 score, and AUC (>97% across metrics). According to SHAP, anger issues, depression, and social isolation emerge as top predictors of suicide risk, while individuals with high incomes, esteemed professions, and higher education present the lowest risk. Our findings underscore the effectiveness of ML and XAI in suicide risk assessment, offering valuable insights for psychiatrists and facilitating informed clinical decisions.

Authors

  • Hao Tang
    Department of Urology, Eastern Theater General Hospital of Chinese People's Liberation Army, Nanjing， Jiangsu 210000, China.
  • Aref Miri Rekavandi
    Department of Computer Science and Software Engineering, The University of Western Australia, Perth, Australia.
  • Dharjinder Rooprai
    Armadale Mental Health Service, Perth, Australia. dharjinder.rooprai@health.wa.gov.au.
  • Girish Dwivedi
    Department of Medicine, The University of Western Australia, 35 Stirling Highway, CRAWLEY Western Australia 6009, Australia.
  • Frank M Sanfilippo
    Cardiovascular Epidemiology Research Centre, School of Population and Global Health, The University of Western Australia, Crawley, Western Australia, Australia.
  • Farid Boussaid
    Department of Electrical, Electronic and Computer Engineering, The University of Western Australia, Australia.
  • Mohammed Bennamoun
    School of Physics, Mathematics and Computing, University of Western Australia, Australia.

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