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A comparative study of machine learning techniques for suicide attempts predictive model.
- Source :
- Health Informatics Journal; Jan-Mar2021, Vol. 27 Issue 1, p1-16, 16p
- Publication Year :
- 2021
-
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]
- Subjects :
- 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
Subjects
Details
- Language :
- English
- ISSN :
- 14604582
- Volume :
- 27
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- Health Informatics Journal
- Publication Type :
- Academic Journal
- Accession number :
- 150395753
- Full Text :
- https://doi.org/10.1177/1460458221989395