A machine learning-based predictive model of non-suicidal self-injurious behavior among college students in Guizhou Province

PAN Chan; LIU Xiaorong; SHI Xiangzi; ZHAO Wenxin; TIAN Meng; CHEN Siyuan; ZHANG Wanzhu

doi:10.16835/j.cnki.1000-9817.2023.08.018

Volume 44 Issue 8

Aug. 2023

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Article Navigation > CHINESE JOURNAL OF SCHOOL HEALTH > 2023 > 44(8): 1198-1202

PAN Chan, LIU Xiaorong, SHI Xiangzi, ZHAO Wenxin, TIAN Meng, CHEN Siyuan, ZHANG Wanzhu. A machine learning-based predictive model of non-suicidal self-injurious behavior among college students in Guizhou Province[J]. CHINESE JOURNAL OF SCHOOL HEALTH, 2023, 44(8): 1198-1202. doi: 10.16835/j.cnki.1000-9817.2023.08.018

Citation:

PAN Chan, LIU Xiaorong, SHI Xiangzi, ZHAO Wenxin, TIAN Meng, CHEN Siyuan, ZHANG Wanzhu. A machine learning-based predictive model of non-suicidal self-injurious behavior among college students in Guizhou Province[J]. CHINESE JOURNAL OF SCHOOL HEALTH, 2023, 44(8): 1198-1202. doi: 10.16835/j.cnki.1000-9817.2023.08.018

Citation:

PAN Chan, LIU Xiaorong, SHI Xiangzi, ZHAO Wenxin, TIAN Meng, CHEN Siyuan, ZHANG Wanzhu. A machine learning-based predictive model of non-suicidal self-injurious behavior among college students in Guizhou Province[J]. CHINESE JOURNAL OF SCHOOL HEALTH, 2023, 44(8): 1198-1202. doi: 10.16835/j.cnki.1000-9817.2023.08.018

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A machine learning-based predictive model of non-suicidal self-injurious behavior among college students in Guizhou Province

doi: 10.16835/j.cnki.1000-9817.2023.08.018

1.
School of Public Health, Guizhou Medical University, Guiyang(550025), China

Received Date: 2023-05-14
Rev Recd Date: 2023-07-10

Available Online: 2023-08-26

Publish Date: 2023-08-25

Abstract

Abstract

Objective To explore the effectiveness of machine learning algorithms in predicting non-suicidal self-injury (NSSI) behavior among college students, and to analyze the influencing factors of NSSI behavior, thus providing a reference for promoting psychological well-being. Methods In December 2022, a stratified random cluster sampling method was used to select 835 college students from a university in Guizhou Province, China. The Adolescent Self-injury Scale, Family Function Assessment Scale, and Emotion Regulation Self-efficacy Scale were used to evaluate the participants. Demographic characteristics, family factors, and emotional factors were taken as independent variables, while the dependent variable was whether college students exhibited NSSI behavior. Machine learning algorithms, including Logistic regression, support vector machine (SVM), decision trees, algorithm gradient boosting trees, random forests, and AdaBoost, were used to construct predictive models. Results The detection rate of NSSI behavior among the college students was 23.23% (194 individuals). The NSSI behavior group scored higher than the non-NSSI behavior group in total family function, emotional communication, egoism, and family rules (t=3.02, 3.35, 2.23, 2.87, P < 0.05). On the other hand, the non-NSSI behavior group scored higher than the NSSI behavior group in total emotion regulation self-efficacy, managing negative emotion self-efficacy, and expressing positive emotion self-efficacy (t=-5.04, -5.48, -2.43, P < 0.05). The recall rates of random forests, SVM, Logistic regression, decision trees, algorithm gradient boosting trees, and AdaBoost were 84.3%, 90.6%, 73.4%, 87.5%, 95.3%, 89.0%, respectively. The F1 scores were 84.4%, 92.1%, 71.2%, 79.4%, 91.7%, 89.1%, respectively. The respective precision rates were 84.4%, 93.5%, 69.1%, 72.7%, 88.4%, 89.1%. The AUC scores were 0.845, 0.922, 0.706, 0.776, 0.915, and 0.891, respectively. Conclusion Compared to the algorithm gradient boosting tree, random forest, Logistic regression, and AdaBoost models, the SVM model has a better predictive effect on whether college students in Guizhou Province exhibits NSSI behavior. It is recommended to use an appropriate model to identify students at risk of NSSI behavior as early as possible and provide psychological crisis interventions to promote their mental health.
- Self-injurious behavior,
- Mental health,
- Forecasting,
- Students

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References(26)

References

[1]	REN Y, LIN M P, LIU Y H, et al. The mediating role of coping strategy in the association between family functioning and non-suicidal self-injury among Taiwanese adolescents[J]. J Clin Psychol, 2018, 74(7): 1246-1257. doi: 10.1002/jclp.22587
[2]	CASSELS M, WILKINSON P. Non-suicidal self-injury in adolescence[J]. Paediatr Child Health, 2016, 26(12): 554-558. doi: 10.1016/j.paed.2016.08.006
[3]	SWANNELL S, V MARTIN G E, PAGE A, et al. Prevalence of non-suicidal self-injury in nonclinical samples: systematic review, Meta-analysis and Meta-regression[J]. Suic Life Threat Behav, 2014, 44(3): 273-303. doi: 10.1111/sltb.12070
[4]	袁勇, 干敏雷, 尹小俭, 等. 青少年非自杀性自伤行为与抑郁症状的相关性[J]. 中国学校卫生, 2023, 44(5): 659-663. doi: 10.16835/j.cnki.1000-9817.2023.05.005 YUAN Y, GAN M L, YIN X J, et al. Correlation between non-suicidal self-injurious behavior and depressive symptoms in adolescents[J]. Chin J Sch Health, 2023, 44(5): 659-663. (in Chinese) doi: 10.16835/j.cnki.1000-9817.2023.05.005
[5]	SCHATTEN H T, ANDOVER M S, ARMEY M F. The roles of social stress and decision-making in non-suicidal self-injury[J]. Psychiatry Res, 2015, 229(3): 983-991. doi: 10.1016/j.psychres.2015.05.087
[6]	KIEKENS G, HASKING P, BOYES M, et al. The associations between non-suicidal self-injury and first onset suicidal thoughts and behaviors[J]. J Affect Disord, 2018, 239: 171-179. doi: 10.1016/j.jad.2018.06.033
[7]	马鸣, 刘欢, 刘润香. 机器学习在大学生自杀意念预测中的应用[J]. 中国学校卫生, 2022, 43(5): 763-767. doi: 10.16835/j.cnki.1000-9817.2022.05.029 MA M, LIU H, LIU R X. Application of machine learning in the prediction of college studentsc suicidal ideation[J]. Chin J Sch Health, 2022, 43(5): 763-767. (in Chinese) doi: 10.16835/j.cnki.1000-9817.2022.05.029
[8]	徐小明. 机器学习方法用于构建青少年自杀自伤行为预警模型研究[D]. 重庆: 重庆医科大学, 2022. XU X M. Study on the machine learing appoachens using for buding the early wearing model of adolesence with suicidal and self-injurious behaviors[D]. Chongqing: Chongqing Medical University, 2022. (in Chinese)
[9]	AMMERMAN B A, JACOBUCCI R, MCCLOSKEY M S. Using exploratory data mining to identify important correlates of non-suicidal self-injury frequency[J]. Psychol Viol, 2018, 8(4): 515-525. doi: 10.1037/vio0000146
[10]	MICHÉ M, STUDERUS E, MEYER A H, et al. Prospective prediction of suicide attempts in community adolescents and young adults, using regression methods and machine learning[J]. J Affect Dis, 2020, 265: 570-578. doi: 10.1016/j.jad.2019.11.093
[11]	冯玉. 青少年自我伤害行为与个体情绪因素和家庭环境因素的关系[D]. 武汉: 华中师范大学, 2008. FENG Y. The relationship between adolescent self-harming behavior and individual emotional and family environmental factors[D]. Wuhan: Huazhong Normal University, 2008. (in Chinese)
[12]	李荣风, 徐夫真, 纪林芹, 等. 家庭功能评定量表的初步修订[J]. 中国健康心理学杂志, 2013, 21(7): 996-1000. https://www.cnki.com.cn/Article/CJFDTOTAL-JKXL201307018.htm LI R F, XU F Z, JI L Q, et al. Revision of Family Assessment Device[J]. Chin J Health Psychol, 2013, 21(7): 996-1000. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JKXL201307018.htm
[13]	王玉洁, 窦凯, 刘毅. 情绪调节自我效能感量表的修订[J]. 广州大学学报(社会科学版), 2013, 12(1): 45-50. https://www.cnki.com.cn/Article/CJFDTOTAL-GZDX201301007.htm WANG Y J, DOU K, LIU Y. Revision of the Scale Regulatory Emotional Self-efficacy[J]. J Guangzhou Univ(Soc Sci Edit), 2013, 12(1): 45-50. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-GZDX201301007.htm
[14]	熊红星, 张璟, 郑雪. 方法影响结果?方法变异的本质、影响及控制[J]. 心理学探新, 2013, 33(3): 195-199. https://www.cnki.com.cn/Article/CJFDTOTAL-XLXT201303002.htm XIONG H X, ZHANG J, ZHENG X. Methodology influences results? The nature, impact, and control of method variation[J]. Psychol Inquiry, 2013, 33(3): 195-199. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-XLXT201303002.htm
[15]	李磊, 黄水平. 支持向量机原理及其在医学分类中的应用[J]. 中国卫生统计, 2009, 26(1): 22-25. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGWT200901008.htm LI L, HUANG S P. Support vector machine principle and its application in medical classification[J]. Chin J Health Stat, 2009, 26(1): 22-25. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGWT200901008.htm
[16]	BADILLO S, BANFAI B, BIRZELE F, et al. An introduction to machine learning[J]. Clin Pharmacol Therapeut, 2020, 107(4): 871-885.
[17]	CHAWLA N V, BOWYER K W, HALL L O, et al. SMOTE: synthetic minority over-sampling technique[J]. J Artif Intell Res, 2002(16): 321-357.
[18]	顾怀婷, 刘根义, 李晶. 大学生非自杀性自伤行为与抑郁、述情障碍的相关性[J]. 中国健康心理学杂志, 2018, 26(1): 6-9. https://www.cnki.com.cn/Article/CJFDTOTAL-JKXL201801003.htm GU H T, LIU G Y, LI J. Correlation of non-suicidal self-injurious behaviors with depression and narrative disorders in college students[J]. Chin J Health Psychol, 2018, 26(1): 6-9. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JKXL201801003.htm
[19]	潘珍, 毛绍菊, 唐寒梅, 等. 中国大学生非自杀性自伤检出率的Meta分析[J]. 中国学校卫生, 2016, 37(6): 878-881. doi: 10.16835/j.cnki.1000-9817.2016.06.020 PAN Z, MAO S J, TANG H M, et al. Meta-analysis of the detection rate of non-suicidal self-injury among Chinese college students[J]. Chin J Sch Health, 2016, 39(6): 878-881. (in Chinese) doi: 10.16835/j.cnki.1000-9817.2016.06.020
[20]	苟双玉, 杜美杰, 刘晓容, 等. 中国青少年非自杀性自伤检出率的Meta分析[J]. 现代预防医学, 2023, 50(2): 263-271. https://www.cnki.com.cn/Article/CJFDTOTAL-XDYF202302013.htm GOU S Y, DU M J, LIU X R, et al. Meta-analysis of the detection rate of non-suicidal self-injury among Chinese adolescents[J]. Mod Prev Med, 2023, 50(2): 263-271. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-XDYF202302013.htm
[21]	ROTHMAN S, SHER L. Suicide prevention in the COVID-19 era[J]. Prev Med, 2021, 152(Pt 1): 106-547.
[22]	梁楷利, 胡蜀萍, 李玉星, 等. 中小学生非自杀性自伤行为家庭环境因素分析[J]. 现代预防医学, 2021, 48(2): 304-307. https://www.cnki.com.cn/Article/CJFDTOTAL-XDYF202102029.htm LIANG K L, HU S P, LI Y X, et al. Family environment factors of non-suicidal self-injury behaviors among primary and middle school students[J]. Mod Prev Med, 2021, 48(2): 304-307. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-XDYF202102029.htm
[23]	张桂梅, 向婷, 蔡艺娴, 等. 青少年非自杀性自伤患者父母人格特征及认知调节情绪策略[J]. 中国学校卫生, 2022, 43(10): 1525-1528. doi: 10.16835/j.cnki.1000-9817.2022.10.020 ZHANG G M, XIANG T, CAI Y X, et al. Personality and cognitive regulation emotion strategies among parents of adolescents with non-suicidal self-injury[J]. Chin J Sch Health, 2022, 43(10): 1525-1528. (in Chinese) doi: 10.16835/j.cnki.1000-9817.2022.10.020
[24]	RAJULA H S R, VERLATO G, MANCHIA M, et al. Comparison of conventional statistical methods with machine learning in medicine: diagnosis, drug development, and treatment[J]. Medicina(Kaunas), 2020, 56(9): 455.
[25]	LINTHICUM P K, SCHAFER K M, RIBEIRO J D. Machine learning in suicide science: applications and ethics[J]. Behav Sci Law, 2019, 37(3): 214-222.
[26]	GRENDAS L N, CHIAPELLA L, RODANTE D E, et al. Comparison of traditional model-based statistical methods with machine learning for the prediction of suicide behaviour[J]. J Psychiatr Res, 2022, 145: 85-91.