A comparative study of machine learning techniques for suicide attempts predictive model

Nordin, Noratikah; Zainol, Zurinahni; Noor, Mohd Halim Mohd; Fong, Chan Lai

doi:10.1177/1460458221989395

Cited by 19 publications

(5 citation statements)

References 31 publications

(71 reference statements)

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“…XGBoost-2 resulted from adding features of cognitive function (SST, IGT) to XGBoost-1. Our ML classifiers presented relatively good performance in the task of classifying suicide attempters in MDD patients in accordance with previous studies that used shallow or deep learning algorithms [ 17 , 36 , 37 ]. According to the statistical indicators, ROC curve, DCA, and NRI, when cognitive function was incorporated into XGBoost model, it exhibited improved model fit and superior predictive accuracy and improved patients net benefits, while maintaining the same level of sensitivity for DSA.…”

Section: Discussionsupporting

confidence: 87%

Can cognition help predict suicide risk in patients with major depressive disorder? A machine learning study

et al. 2022

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Background Previous studies suggest that deficits in cognition may increase the risk of suicide. Our study aims to develop a machine learning (ML) algorithm-based suicide risk prediction model using cognition in patients with major depressive disorder (MDD). Methods Participants comprised 52 depressed suicide attempters (DSA) and 61 depressed non-suicide attempters (DNS), and 98 healthy controls (HC). All participants were required to complete a series of questionnaires, the Suicide Stroop Task (SST) and the Iowa Gambling Task (IGT). The performance in IGT was analyzed using repeated measures ANOVA. ML with extreme gradient boosting (XGBoost) classification algorithm and locally explanatory techniques assessed performance and relative importance of characteristics for predicting suicide attempts. Prediction performances were compared with the area under the curve (AUC), decision curve analysis (DCA), and net reclassification improvement (NRI). Results DSA and DNS preferred to select the card from disadvantageous decks (decks "A" + "B") under risky situation (p = 0.023) and showed a significantly poorer learning effect during the IGT (F = 2.331, p = 0.019) compared with HC. Performance of XGBoost model based on demographic and clinical characteristics was compared with that of the model created after adding cognition data (AUC, 0.779 vs. 0.819, p > 0.05). The net benefit of model was improved and cognition resulted in continuous reclassification improvement with NRI of 5.3%. Several clinical dimensions were significant predictors in the XGBoost classification algorithm. Limitations A limited sample size and failure to include sufficient suicide risk factors in the predictive model. Conclusion This study demonstrate that cognitive deficits may serve as an important risk factor to predict suicide attempts in patients with MDD. Combined with other demographic characteristics and attributes drawn from clinical questionnaires, cognitive function can improve the predictive effectiveness of the ML model. Additionally, explanatory ML models can help clinicians detect specific risk factors for each suicide attempter within MDD patients. These findings may be helpful for clinicians to detect those at high risk of suicide attempts quickly and accurately, and help them make proactive treatment decisions.

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Section: Discussionsupporting

confidence: 87%

Can cognition help predict suicide risk in patients with major depressive disorder? A machine learning study

et al. 2022

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“…[50,[64][65][66] Several studies have shown that the prediction performance of suicide risk prediction models varies according to different populations. [67][68][69] Therefore, SVM can be used to develop the best predictive algorithm to identify at-risk university students with suicidal ideation/behaviors. Health professionals and university authorities can utilize this computerized system to identify people, specifically university students, at risk for suicidal events.…”

Section: Discussionmentioning

confidence: 99%

Machine learning approaches for predicting suicidal behaviors among university students in Bangladesh during the COVID-19 pandemic: A cross-sectional study

et al. 2023

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Psychological and behavioral stress has increased enormously during Coronavirus Disease 2019 (COVID-19) pandemic. However, early prediction and intervention to address psychological distress and suicidal behaviors are crucial to prevent suicide-related deaths. This study aimed to develop a machine algorithm to predict suicidal behaviors and identify essential predictors of suicidal behaviors among university students in Bangladesh during the COVID-19 pandemic. An anonymous online survey was conducted among university students in Bangladesh from June 1 to June 30, 2022. A total of 2391 university students completed and submitted the questionnaires. Five different Machine Learning models (MLMs) were applied to develop a suitable algorithm for predicting suicidal behaviors among university students. In predicting suicidal behaviors, the most crucial background and demographic features were relationship status, friendly environment in the family, family income, family type, and sex. In addition, features related to the impact of the COVID-19 pandemic were identified as job loss, economic loss, and loss of family/relatives due to COVID-19. Moreover, factors related to mental health include depression, anxiety, stress, and insomnia. The performance evaluation and comparison of the MLM showed that all models behaved consistently and were comparable in predicting suicidal risk. However, the Support Vector Machine was the best and most consistent performing model among all MLMs in terms of accuracy (79%), Kappa (0.59), receiver operating characteristic (0.89), sensitivity (0.81), and specificity (0.81). Support Vector Machine is the best-performing model for predicting suicidal risks among university students in Bangladesh and can help in designing appropriate and timely suicide prevention interventions.

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“…Random forest is an algorithm based on the bagging algorithm [22]. The random forest is one of the ensembles learning methods that consist of a set of decision trees that are generated by the recursive sampling of bootstrapped samples of training data [23]. In this algorithm, B separate training sets which are bootstrapped from the training set and then the predictive functions are generated.…”

Section: -3-random Forestmentioning

confidence: 99%

Associated Patterns and Predicting Model of Life Trauma, Depression, and Suicide Using Ensemble Machine Learning

Aekwarangkoon

Thanathamathee

2022

Emerg Sci J

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This study aimed to find associated patterns by association rule mining and propose a prediction model using ensemble learning methods of high levels of trauma items affecting depression and suicide among primary school students in Thai rural extended opportunity schools. Our proposed methods were different from others that have analysed the relationship of high life trauma leading to depression and suicide by using statistical analysis. We found strongly associated patterns and effects among primary students’ trauma, depression, and suicide. The trauma of psychological abuse and neglect may result in suicide, whereas psychological abuse, neglect, and the experience of self-harm are also likely to result in the increased severity of traumatic events in life. The trauma of physical and sexual abuse, neglect, helplessness, feeling worthless, being weak, and self-harm were associated with depression. Our research discovered new knowledge that the risk of suicide arises from two extreme types of trauma: when children’s safety is frequently threatened and the family communicates frequently using rude or abusive words; these traumas may not merely correlate with depression but may ultimately result in suicide. Moreover, this study discovered 7 highly important trauma items and 4 suicide items for predicting depression and suicide using the Random Forest technique. We found that the Random Forest technique performed well in predicting depression and suicide. The predicted depression results show that the overall accuracy was 85.84%, precision was 89.33%, and recall was 75.28%. The predicted suicide results show that the overall accuracy was 91.28%, precision was 89.05%, and recall was 84.72%. From these results, we identified high life trauma affecting depression and suicide, which are very beneficial to practitioners to use in preliminary screening. In addition, those involved need to be aware and attentive in counselling these people with these symptoms in time. Doi: 10.28991/ESJ-2022-06-04-02 Full Text: PDF

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A comparative study of machine learning techniques for suicide attempts predictive model

Cited by 19 publications

References 31 publications

Can cognition help predict suicide risk in patients with major depressive disorder? A machine learning study

Can cognition help predict suicide risk in patients with major depressive disorder? A machine learning study

Machine learning approaches for predicting suicidal behaviors among university students in Bangladesh during the COVID-19 pandemic: A cross-sectional study

Associated Patterns and Predicting Model of Life Trauma, Depression, and Suicide Using Ensemble Machine Learning

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