Nasrin Matinnia , Behnaz Alafchi , Arya Haddadi , Ali Ghaleiha , Hasan Davari , Manochehr Karami , Zahra Taslimi , Mohammad Reza Afkhami , Saeid Yazdi-Ravandi
{"title":"Anticipating influential factors on suicide outcomes through machine learning techniques: Insights from a suicide registration program in western Iran","authors":"Nasrin Matinnia , Behnaz Alafchi , Arya Haddadi , Ali Ghaleiha , Hasan Davari , Manochehr Karami , Zahra Taslimi , Mohammad Reza Afkhami , Saeid Yazdi-Ravandi","doi":"10.1016/j.ajp.2024.104183","DOIUrl":null,"url":null,"abstract":"<div><p>Suicide is a global public health concern, with increasing rates observed in various regions, including Iran. This study focuses on the province of Hamadan, Iran, where suicide rates have been on the rise. The research aims to predict factors influencing suicide outcomes by leveraging machine learning techniques on the Hamadan Suicide Registry Program data collected from 2016 to 2017. The study employs Naïve Bayes and Random Forest algorithms, comparing their performance to logistic regression. Results highlight the superiority of the Random Forest model. Based on the variable importance and multiple logistic regression analyses, the most important determinants of suicide outcomes were identified as suicide method, age, and timing of attempts, income, and motivation. The findings emphasize the cultural context's impact on suicide methods and underscore the importance of tailoring prevention programs to address specific risk factors, especially for older individuals. This study contributes valuable insights for suicide prevention efforts in the region, advocating for context-specific interventions and further research to refine predictive models and develop targeted prevention strategies.</p></div>","PeriodicalId":8543,"journal":{"name":"Asian journal of psychiatry","volume":"100 ","pages":"Article 104183"},"PeriodicalIF":3.8000,"publicationDate":"2024-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Asian journal of psychiatry","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1876201824002764","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PSYCHIATRY","Score":null,"Total":0}
引用次数: 0
Abstract
Suicide is a global public health concern, with increasing rates observed in various regions, including Iran. This study focuses on the province of Hamadan, Iran, where suicide rates have been on the rise. The research aims to predict factors influencing suicide outcomes by leveraging machine learning techniques on the Hamadan Suicide Registry Program data collected from 2016 to 2017. The study employs Naïve Bayes and Random Forest algorithms, comparing their performance to logistic regression. Results highlight the superiority of the Random Forest model. Based on the variable importance and multiple logistic regression analyses, the most important determinants of suicide outcomes were identified as suicide method, age, and timing of attempts, income, and motivation. The findings emphasize the cultural context's impact on suicide methods and underscore the importance of tailoring prevention programs to address specific risk factors, especially for older individuals. This study contributes valuable insights for suicide prevention efforts in the region, advocating for context-specific interventions and further research to refine predictive models and develop targeted prevention strategies.
期刊介绍:
The Asian Journal of Psychiatry serves as a comprehensive resource for psychiatrists, mental health clinicians, neurologists, physicians, mental health students, and policymakers. Its goal is to facilitate the exchange of research findings and clinical practices between Asia and the global community. The journal focuses on psychiatric research relevant to Asia, covering preclinical, clinical, service system, and policy development topics. It also highlights the socio-cultural diversity of the region in relation to mental health.