1. A comparative study of machine learning techniques for suicide attempts predictive model.
- Author
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Nordin, Noratikah, Zainol, Zurinahni, Mohd Noor, Mohd Halim, and Fong, Chan Lai
- Subjects
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SUICIDE risk factors , *SUPPORT vector machines , *ACADEMIC medical centers , *MACHINE learning , *RACE , *RANDOM forest algorithms , *RISK assessment , *COMPARATIVE studies , *SUICIDAL ideation , *SEVERITY of illness index , *MENTAL depression , *DESCRIPTIVE statistics , *PREDICTION models , *PSYCHOLOGY & religion , *RECEIVER operating characteristic curves , *PREDICTIVE validity , *ALGORITHMS , *DATA mining , *EVALUATION - Abstract
Current suicide risk assessments for predicting suicide attempts are time consuming, of low predictive value and have inadequate reliability. This paper aims to develop a predictive model for suicide attempts among patients with depression using machine learning algorithms as well as presents a comparative study on single predictive models with ensemble predictive models for differentiating depressed patients with suicide attempts from non-suicide attempters. We applied and trained eight different machine learning algorithms using a dataset that consists of 75 patients diagnosed with a depressive disorder. A recursive feature elimination was used to reduce the features via three-fold cross validation. An ensemble predictive models outperformed the single predictive models. Voting and bagging revealed the highest accuracy of 92% compared to other machine learning algorithms. Our findings indicate that history of suicide attempt, religion, race, suicide ideation and severity of clinical depression are useful factors for prediction of suicide attempts. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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