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ORIGINAL RESEARCH article

Front. Psychiatry
Sec. Public Mental Health
Volume 15 - 2024 | doi: 10.3389/fpsyt.2024.1521025
This article is part of the Research Topic The Intersection of Psychology, Healthy Behaviors, and its Outcomes View all 49 articles

Research on prediction model of adolescent suicide and self-injury behavior based on machine learning algorithm

Provisionally accepted
Yao Gan Yao Gan Li Kuang Li Kuang *Xiaoming Xu Xiaoming Xu Ming Ai Ming Ai Jinglan He Jinglan He Wo Wang Wo Wang Su Hong Su Hong Jianmei Chen Jianmei Chen Cao Jun Cao Jun Qi Zhang Qi Zhang
  • First Affiliated Hospital of Chongqing Medical University, Chongqing, China

The final, formatted version of the article will be published soon.

    Objective To explore the risk factors that affect adolescents' suicidal and self-injurious behaviors, and to construct a prediction model for adolescents' suicidal and self-injurious behaviors based on machine learning algorithms. Methods Stratified cluster sampling was used to select high school students in Chongqing, yielding 3000 valid questionnaires.Based on whether students had engaged in suicide or self-injury, they were categorized into a suicide/self-injury group (n=78) and a non-suicide/self-injury group (n=2922). Gender, age, insomnia, and mental illness data were compared between the two groups, and a logistic regression model was used to analyze independent risk factors for adolescent suicidal and self-injurious behavior. Six methods-multi-level perceptron, random forest, K-nearest neighbor, support vector machine, logistic regression, and extreme gradient boosting-were used to build predictive models. Various model indicators for suicidal and self-injurious behavior were compared across the six algorithms using a confusion matrix to identify the optimal model. ResultIn the self-injury and suicide group, the proportions of adolescent males, late adolescence, insomnia, and mental illness were significantly higher than in the non-suicide and self-injury group (P <0.05). Compared with the non-suicidal self-injury group, this group also showed significantly increased scores in cognitive subscales, impulsivity, psychoticism, introversion-extroversion, neuroticism, interpersonal sensitivity, depression, anxiety, hostility, terror, and paranoia (P <0.05). These statistically significant variables were analyzed in a logistic regression model, revealing that gender, impulsivity, psychoticism, neuroticism, interpersonal sensitivity, depression, and paranoia are independent risk factors for adolescent suicide and self-injury. The logistic regression model achieved the highest sensitivity and specificity in predicting adolescent suicide and self-injury behavior (0.9948 and 0.9981, respectively).Performance of the random forest, multi-level perceptron, and extreme gradient models was acceptable, while the K-nearest neighbor algorithm and support vector machine performed poorly.

    Keywords: Machine learning algorithm, suicidal and self-injurious behavior, adolescents, Risk factors, Prediction model

    Received: 01 Nov 2024; Accepted: 30 Dec 2024.

    Copyright: © 2024 Gan, Kuang, Xu, Ai, He, Wang, Hong, Chen, Jun and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Li Kuang, First Affiliated Hospital of Chongqing Medical University, Chongqing, China

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