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

Saifon Aekwarangkoon, Putthiporn Thanathamathee


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

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Association Rule Mining; Trauma; Depression; Suicide; Random Forest; FP-Growth; Machine Learning.


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DOI: 10.28991/ESJ-2022-06-04-02


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