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

Saifon Aekwarangkoon, Putthiporn Thanathamathee

Abstract


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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Keywords


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

References


Charak, R., Byllesby, B. M., Roley, M. E., Claycomb, M. A., Durham, T. A., Ross, J., Armour, C., & Elhai, J. D. (2016). Latent classes of childhood poly-victimization and associations with suicidal behavior among adult trauma victims: Moderating role of anger. Child Abuse and Neglect, 62, 19–28. doi:10.1016/j.chiabu.2016.10.010.

Alter, S., Wilson, C., Sun, S., Harris, R. E., Wang, Z., Vitale, A., Hazlett, E. A., Goodman, M., Ge, Y., Yehuda, R., Galfalvy, H., & Haghighi, F. (2021). The association of childhood trauma with sleep disturbances and risk of suicide in US veterans. Journal of Psychiatric Research, 136, 54–62. doi:10.1016/j.jpsychires.2021.01.030.

Pham, T. S., Qi, H., Chen, D., Chen, H., & Fan, F. (2021). Prevalences of and correlations between childhood trauma and depressive symptoms, anxiety symptoms, and suicidal behavior among institutionalized adolescents in Vietnam. Child Abuse and Neglect, 115, 105022. doi:10.1016/j.chiabu.2021.105022.

Behr Gomes Jardim, G., Novelo, M., Spanemberg, L., von Gunten, A., Engroff, P., Nogueira, E. L., & Cataldo Neto, A. (2018). Influence of childhood abuse and neglect subtypes on late-life suicide risk beyond depression. Child Abuse and Neglect, 80, 249–256. doi:10.1016/j.chiabu.2018.03.029.

Passos, I. C., Mwangi, B., Cao, B., Hamilton, J. E., Wu, M. J., Zhang, X. Y., Zunta-Soares, G. B., Quevedo, J., Kauer-Sant’Anna, M., Kapczinski, F., & Soares, J. C. (2016). Identifying a clinical signature of suicidality among patients with mood disorders: A pilot study using a machine learning approach. Journal of Affective Disorders, 193, 109–116. doi:10.1016/j.jad.2015.12.066.

Sekowski, M., Gambin, M., Cudo, A., Wozniak-Prus, M., Penner, F., Fonagy, P., & Sharp, C. (2020). The relations between childhood maltreatment, shame, guilt, depression and suicidal ideation in inpatient adolescents. Journal of Affective Disorders, 276, 667–677. doi:10.1016/j.jad.2020.07.056.

De Little, M. (2019). Where Words Can’t Reach: Neuroscience and the Satir Model in the Sand Tray (3rd Ed.). Dr. Madeleine De Little & Associates, Vancouver, Canada.

Vinney, C. (2019). Freud: ID, Ego, and Superego Explained. Available online: https://www.thoughtco.com/id-ego-and-superego-4582342/ (accessed on January 2022).

Trangkasombat, U., & Likanapichitkul, D. (1996). Depressive Symptoms in Children : A Study Using the Children’s Depression Inventory. Journal Psychiatry Association Thailand, 41(4), 221–230.

DeCou, C. R., & Lynch, S. M. (2019). Emotional reactivity, trauma-related distress, and suicidal ideation among adolescent inpatient survivors of sexual abuse. Child Abuse and Neglect, 89, 155–164. doi:10.1016/j.chiabu.2019.01.012.

Daray, F. M., Rojas, S. M., Bridges, A. J., Badour, C. L., Grendas, L., Rodante, D., Puppo, S., & Rebok, F. (2016). The independent effects of child sexual abuse and impulsivity on lifetime suicide attempts among female patients. Child Abuse and Neglect, 58, 91–98. doi:10.1016/j.chiabu.2016.06.011.

Hataiyusuk, S., & Apinuntavech, S. (2020). Adolescent suicide in Thailand: Incidence, causes and prevention. Siriraj Med Bull, 13(1), 40–47. (In Thai).

Visessuvanapoom, P., & Nakornthap, A. (2020). Life skills of opportunity expansion school students from different backgrounds. Journal of Research Methodology, 33(3), 257–274.

Kuroki, Y. (2015). Risk factors for suicidal behaviors among Filipino Americans: A data mining approach. American Journal of Orthopsychiatry, 85(1), 34–42. doi:10.1037/ort0000018.

DelPozo-Banos, M., John, A., Petkov, N., Berridge, D. M., Southern, K., Loyd, K. L., Jones, C., Spencer, S., & Travieso, C. M. (2018). Using neural networks with routine health records to identify suicide risk: Feasibility study. JMIR Mental Health, 5(2), e10144. doi:10.2196/10144.

Bae, S. M., Lee, S. A., & Lee, S. H. (2015). Prediction by data mining, of suicide attempts in Korean adolescents: A national study. Neuropsychiatric Disease and Treatment, 11, 2367–2375. doi:10.2147/NDT.S91111.

Kharkongor, C., & Nath, B. (2021). Itemset representation and mining the rules for huntington’s dataset. Emerging Science Journal, 5(3), 380–391. doi:10.28991/esj-2021-01284.

Chen, M. S., Han, J., & Yu, P. S. (1996). Data mining: An overview from a database perspective. IEEE Transactions on Knowledge and Data Engineering, 8(6), 866–883. doi:10.1109/69.553155.

Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. ACM sigmod record, 29(2), 1-12. doi:10.1145/335191.335372.

Fahrudin, T. M., Syarif, I., & Barakbah, A. R. (2017). Discovering patterns of NED-breast cancer based on association rules using apriori and FP-growth. Proceedings - International Electronics Symposium on Knowledge Creation and Intelligent Computing, IES-KCIC 2017, 2017-January, 132–139. doi:10.1109/KCIC.2017.8228576.

Haque, U. M., Kabir, E., & Khanam, R. (2021). Detection of child depression using machine learning methods. PLoS ONE, 16(12), e0261131. doi:10.1371/journal.pone.0261131.

Turska, E., Jurga, S., & Piskorski, J. (2021). Mood disorder detection in adolescents by classification trees, random forests and xgboost in presence of missing data. Entropy, 23(9), 1210. doi:10.3390/e23091210.

Nordin, N., Zainol, Z., Mohd Noor, M. H., & Lai Fong, C. (2021). A comparative study of machine learning techniques for suicide attempts predictive model. Health Informatics Journal, 27(1), 1460458221989395. doi:10.1177/1460458221989395.

Liu, Y., Gu, Y., Nguyen, J. C., Li, H., Zhang, J., Gao, Y., & Huang, Y. (2017). Symptom severity classification with gradient tree boosting. Journal of Biomedical Informatics, 75, S105–S111. doi:10.1016/j.jbi.2017.05.015.

Chuatai, N., & Chandarasiri, P. (2017). Development of small t trauma assessing instrument for secondary school students. Chulalongkorn Medical Journal, 61(1), 129-142.

Kovacs, M. (1992). Children’s depression inventory. Acta Paedopsychiatrica: International Journal of Child & Adolescent Psychiatry.

Tangseree, T., Arunpongpaisal, S., Chiravatkul, A., Krisanaprakornkit, T., Kittiwattanagul, K., Pratchayakhup, W., Lausuangkul, A., Kaewhao, S., Huttapanom, W., Pratchayakhup, P., & Mongkol, A. (2009). Development for depression and suicidal risk screening test (version 2009). J Psychiatr AssocThailand, 54(3), 287-298. (In Thai).

RapidMiner (2022). RapidMiner Studio version 9.8. Available online: https://rapidminer.com/ (accessed on April 2021).

Park, C., Park, I.-H., Yoo, T., Kim, H., Ryu, S., Lee, J. Y., Kim, J. M., & Kim, S. W. (2021). Association between Childhood Trauma and Suicidal Behavior in the General Population. Chonnam Medical Journal, 57(2), 126. doi:10.4068/cmj.2021.57.2.126.

Yin, H., Galfalvy, H., Zhang, B., Tang, W., Xin, Q., Li, E., Xue, X., Li, Q., Ye, J., Yan, N., & Mann, J. J. (2020). Interactions of the GABRG2 polymorphisms and childhood trauma on suicide attempt and related traits in depressed patients. Journal of Affective Disorders, 266, 447–455. doi:10.1016/j.jad.2020.01.126.

Shukla, M., Ahmad, S., Singh, J. V., Shukla, N. K., & Shukla, R. (2019). Factors Associated with Depression among School-going Adolescent Girls in a District of Northern India: A Cross-sectional Study. Indian Journal of Psychological Medicine, 41(1), 46–53. doi:10.4103/IJPSYM.IJPSYM_211_18.

Nalugya-Sserunjogi, J., Rukundo, G. Z., Ovuga, E., Kiwuwa, S. M., Musisi, S., & Nakimuli-Mpungu, E. (2016). Prevalence and factors associated with depression symptoms among school-going adolescents in Central Uganda. Child and Adolescent Psychiatry and Mental Health, 10(1), 2–8. doi:10.1186/s13034-016-0133-4.

Kaur, A., & Kaur, I. (2018). An empirical evaluation of classification algorithms for fault prediction in open source projects. Journal of King Saud University - Computer and Information Sciences, 30(1), 2–17. doi:10.1016/j.jksuci.2016.04.002.

Sawangarreerak, S., & Thanathamathee, P. (2020). Random forest with sampling techniques for handling imbalanced prediction of university student depression. Information, 11(11), 519. doi:10.3390/info11110519.


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

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