[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123315-en":3,"doc-seo-123315-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123315,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","A MACHINE LEARNING FRAMEWORK FOR SUICIDAL THOUGHTS PREDICTION USING LOGISTIC REGRESSION AND SMOTE ALGORITHM","Suicide presents an urgent global public health challenge aligned with SDG 3 mental health and well-being targets. This study predicts suicidal thoughts using machine learning with the 2021 National Women’s Life Experience Survey (SPHPN), covering 11,305 ever-married women aged 15–64, among which 504 (4.5%) reported suicidal thoughts. Binary outcomes (1 vs 0) are modeled using logistic regression and SMOTE-based logistic regression with seven predictors: age, education, residence type, physical/sexual violence, smoking frequency, alcohol consumption, and depression. SMOTE improves sensitivity, false positive rate, balanced accuracy, and Kappa, supporting violence- and mental-health focused interventions. Results also enable identifying high-risk individuals for timely care, and motivate future work with broader predictors and longitudinal data to clarify causality and timing.","A MACHINE LEARNING FRAMEWORK FOR SUICIDAL THOUGHTS PREDICTION USING LOGISTIC REGRESSION  \nAND SMOTE ALGORITHM  \nSarni Maniar Berliana 1*, Omas Bulan Samosir2, Rafidah Abd Karim3, Victoria Pena Valenzuela4, Krismanti Tri Wahyuni5, Andi Alfian6  \n1,5Research Unit of Sustainable Development Goals, Politeknik Statistika STIS Jln. Otto Iskandardinata 63C, Jakarta, 13330, Indonesia 2Demographic Institute, Faculty of Economics and Business, Universitas Indonesia Jln. Prof. Dr. Sumitro Djojohadikusumo UI, Depok, Jawa Barat 16424, Indonesia 3Academy of Language Studies, University Teknologi MARA  \nPerak Branch Tapah Campus, 35400, Malaysia  \n4Department of Public Administration and Governance, College of Social Sciences and Philosophy, Bulacan State University Diversion Road, Malolos, Bulacan, 3000, Philippines  \n6BPS-Statistics of East Luwu Regency  \nJln. Ki Hajar Dewantara, Puncak Indah, Kabupaten Luwu Timur, Sulawesi Selatan, 92936 Indonesia Corresponding author’s e-mail: * [sarni@stis.ac.id](sarni@stis.ac.id)  \nArticle History:  \nReceived: 19th November 2024  \nRevised: 2nd February 2025  \nAccepted: 4th March 2025  \nPublished:1st April 2025  \nKeywords:  \nBalanced Accuracy;  \nImbalanced Data;  \nKappa;  \nMental Health;  \nSDG 3;  \nSensitivity;  \nSpecificity.  \nABSTRACT  \nSuicide, a global health challenge identified in Goal 3 of the global agenda for enhancing worldwide well-being, demands urgent attention. This study focused on predicting suicidal thoughts using machine learning, leveraging the 2021 National Women's Life Experience Survey (SPHPN) involving women aged 15 to 64. Analyzing 11,305 ever-married women, 504 (4.5%) reported experiencing suicidal thoughts. The outcome variable was binary (1 for suicidal thoughts, 0 for none). The study used seven predictors: age, education level, residence type, physical and sexual violence, smoking frequency, alcohol consumption, and depression. Ordinary logistic regression and SMOTE-based logistic regression were applied. The former identified physical violence, depression, and sexual violence as crucial factors, while the latter emphasized physical violence, sexual violence, and age. In cases of class imbalance, the SMOTE-enhanced model exhibited improved performance in terms of sensitivity, false positive rate, balanced accuracy, and Kappa statistic, with lower standard errors of parameter estimates. The findings highlight the importance of addressing violence and mental health in policies aimed at reducing suicidal thoughts among women. Policymakers can use these insights to develop targeted interventions, and healthcare providers can identify high-risk individuals for timely interventions. Community programs and public health campaigns should promote mental well-being and prevent suicidal behaviors using these findings. Future research should include more predictors, diverse populations, and longitudinal data to better understand causal relationships and timing. Interdisciplinary collaboration and advanced machine learning echniques can enhance predictive accuracy and model interpretability  \nThis article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-ShareAlike 4.0 International License.  \nHow to cite this article:  \nS. M. Berliana, O. B. Samosir, R. A. Karim, V. P. Valenzuela, K. T. Wahyuni and A. Alfian.,“A MACHINE LEARNING FRAMEWORK FOR SUICIDAL THOUGHTS PREDICTION USING LOGISTIC REGRESSION AND SMOTE ALGORITHM,” BAREKENG: J. Math. & App., vol. 19, iss. 2, pp. 1409-1420, June, 2025.  \nCopyright © 2025 Author(s)  \nJournal homepage: [https://ojs3.unpatti.ac.id/index.php/barekeng/](https://ojs3.unpatti.ac.id/index.php/barekeng/)  \nJournal e-mail: [barekeng.math@yahoo.com](barekeng.math@yahoo.com); [barekeng.journal@mail.unpatti.ac.id](barekeng.journal@mail.unpatti.ac.id)  \nResearch Article ∙ Open Access  \n1. INTRODUCTION  \nThe intentional killing of oneself is known as suicide [1] . Globally, suicide is a persistent public health ","cbCailkr1CY5EvOE","https://ap.wps.com/l/cbCailkr1CY5EvOE","pdf",487761,1,12,"English","en",105,"# Introduction\n## Background and significance of suicidal ideation\n## Link to mental health and SDG 3","[{\"question\":\"What dataset and population are used for suicidal thoughts prediction?\",\"answer\":\"The study uses the 2021 National Women’s Life Experience Survey (SPHPN) covering women aged 15 to 64, analyzing 11,305 ever-married women.\"},{\"question\":\"Which modeling approaches are compared in the study?\",\"answer\":\"Ordinary logistic regression is compared with SMOTE-based logistic regression to handle class imbalance.\"},{\"question\":\"How does the SMOTE-enhanced model change performance under imbalance?\",\"answer\":\"The SMOTE model shows improved sensitivity, false positive rate, balanced accuracy, and Kappa, along with lower standard errors of parameter estimates.\"}]","A MACHINE LEARNING FRAMEWORK FOR SUICIDAL THOUGHTS PREDICTION USING LOGISTIC REGRESSION AND SMOTE ALGORITHM | PDF",1785815892,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"a-machine-learning-framework-for-suicidal-thoughts-prediction-using-logistic-regression-and-smote-algorithm","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/a-machine-learning-framework-for-suicidal-thoughts-prediction-using-logistic-regression-and-smote-algorithm/123315/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What dataset and population are used for suicidal thoughts prediction?","Question",{"text":75,"@type":76},"The study uses the 2021 National Women’s Life Experience Survey (SPHPN) covering women aged 15 to 64, analyzing 11,305 ever-married women.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which modeling approaches are compared in the study?",{"text":80,"@type":76},"Ordinary logistic regression is compared with SMOTE-based logistic regression to handle class imbalance.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the SMOTE-enhanced model change performance under imbalance?",{"text":84,"@type":76},"The SMOTE model shows improved sensitivity, false positive rate, balanced accuracy, and Kappa, along with lower standard errors of parameter estimates.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]