[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124631-en":3,"doc-seo-124631-105":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},124631,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Predicting Suicide Risk Among Youths Using Machine Learning Methods","Suicide is the second leading cause of death among youths in the USA, and accurate risk prediction remains challenging for clinical diagnosis. This thesis compares six classification algorithms—naïve Bayes, logistic regression, multilayer perceptron, AdaBoost, random forest, and bagging—using the YRBSS dataset while evaluating multiple data handling techniques. The study applies missing value imputation, feature selection, and sampling to address class imbalance, then measures performance with accuracy, precision, recall, F2 score, and AUROC. Results show random forest with undersampling achieves the strongest recall and AUROC, followed by logistic regression and Ada.","The University of Southern Mississippi  \nThe Aquila Digital Community  \nMaster's Theses  \nSpring 5-9-2023  \nPredicting Suicide Risk Among Youths Using Machine Learning Methods  \nSaswati Bhattacharjee  \nFollow this and additional works at: [https://aquila.usm.edu/masters_theses](https://aquila.usm.edu/masters_theses)  \n Part of the Other Computer Engineering Commons  \nRecommended Citation  \nBhattacharjee, Saswati, \"Predicting Suicide Risk Among Youths Using Machine Learning Methods\" (2023) . Master 's Theses. 973.  \n[https://aquila.usm.edu/masters_theses/973](https://aquila.usm.edu/masters_theses/973)  \nThis Masters Thesis is brought to you for free and open access by The Aquila Digital Community. It has been accepted for inclusion in Master's Theses by an authorized administrator of The Aquila Digital Community. For more information, please contact [aquilastaff@usm.edu](aquilastaff@usm.edu).  \nPredicting Suicide Risk Among Youths Using Machine Learning  \nMethods  \nBy  \nSaswati Bhattacharjee  \nA Thesis  \nSubmitted to the Graduate School, the College of Arts and Sciences.  \nand the School of Computing Sciences and Computer Engineering at The University of Southern Mississippi in Fulfillment of the Requirements for the Degree of Master of Science  \nApproved by:  \nDr. Chaoyang Zhang, Committee Chair Dr. Sarah Lee  \nDr. Ahmed Sherif  \nCOPYRIGHT BY  \nSaswati Bhattacharjee  \n2023  \nPublished by the Graduate School  \nABSTRACT  \nSuicide is the second leading cause of death among youths in the USA. Although machine learning approaches have provided great potential for predicting suicide risk using survey data, prediction accuracy may not meet the need for clinical diagnosis due to the intrinsic characteristics of datasets. In this study, I perform a comparative study of six classification algorithms including naïve Bayes (NB), logistic regression (LR), multilayer perceptron (MLP), AdaBoost (Ada), random forest (RF), and bagging using YRBSS dataset and investigate the effectiveness of several data handling techniques to improve the overall performance of suicide risk prediction.  \nThe dataset consists of 76 health risk-related questions with 13,437 responses collected from 136 high school students in the USA. Various preprocessing techniques such as missing value imputation, feature selection, and sampling techniques for handling the imbalanced ratio of the class label were applied to the dataset. The data was partitioned into a training dataset (70%) anda test dataset (30%) using a stratified partitioning method. The performance of the classifiers was evaluated using five evaluation metrics including accuracy, precision, recall, F2 score, and area under the receiver operating characteristic curve (AUROC) . The result showed that RF classifier with undersampling method achieved the highest recall of 0.84, F2 measure of 0.72, and AUROC of 0.85 followed by LR and Ada classifiers.  \nTherefore, I can conclude that RF, LR, AdaBoost are powerful tools for predicting suicidal tendencies in youth. Feature selection and undersampling methods are crucial preprocessing steps necessary to identify adolescents who are at high suicide risk.  \nAcknowledgment  \nI would like to express my sincere gratitude to my thesis advisor, Dr. Chaoyang (Joe) Zhang, for his unwavering support and guidance throughout this research. His expertise, feedback, and encouragement were invaluable to me and greatly contributed to the completion of this project.  \nI am also grateful to the members of my thesis committee, Dr. Sarah Lee and Dr. Ahmed Sherif, for their constructive feedback and valuable suggestions that have helped me refine my research and writing skills. I also extend my appreciation to the faculty members of the Computer Science department, who have provided me with a strong academic foundation and have challenged me to think critically and analytically.  \nMy deepest gratitude goes to my parents and husband for their love, encouragement, and unwavering support","cbCaitqwY0DpcsQ2","https://ap.wps.com/l/cbCaitqwY0DpcsQ2","pdf",1310100,1,49,"English","en",105,"# ABSTRACT\n# Acknowledgment\n# LIST OF TABLES\n# LIST OF ILLUSTRATIONS\n# LIST OF ABBREVIATION\n# CHAPTER I – INTRODUCTION\n## 1.1 Significance of suicide risk prediction\n## 1.2 Literature review and related studies\n## 1.3 Contribution to the work\n# Chapter II – DATA COLLECTION AND PROCESSING\n## 2.1 Source of the Dataset\n## 2.2 The overview of the Dataset\n## 2.3 Data Preprocessing","[{\"question\":\"Which machine learning algorithms are evaluated for predicting suicide risk among youth?\",\"answer\":\"The thesis compares naïve Bayes, logistic regression, multilayer perceptron, AdaBoost, random forest, and bagging.\"},{\"question\":\"What preprocessing and data handling techniques are used to improve prediction performance?\",\"answer\":\"Missing value imputation, feature selection, and sampling methods are applied to handle the imbalanced class label ratio.\"},{\"question\":\"How is model performance evaluated in the study?\",\"answer\":\"Performance is assessed using accuracy, precision, recall, F2 score, and AUROC, computed after a stratified 70% training and 30% test split.\"}]","Predicting Suicide Risk Among Youths Using Machine Learning Methods | PDF",1785893414,123,{"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},"predicting-suicide-risk-among-youths-using-machine-learning-methods","",{"@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/predicting-suicide-risk-among-youths-using-machine-learning-methods/124631/",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","2026-08-05",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},"Which machine learning algorithms are evaluated for predicting suicide risk among youth?","Question",{"text":75,"@type":76},"The thesis compares naïve Bayes, logistic regression, multilayer perceptron, AdaBoost, random forest, and bagging.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What preprocessing and data handling techniques are used to improve prediction performance?",{"text":80,"@type":76},"Missing value imputation, feature selection, and sampling methods are applied to handle the imbalanced class label ratio.",{"name":82,"@type":73,"acceptedAnswer":83},"How is model performance evaluated in the study?",{"text":84,"@type":76},"Performance is assessed using accuracy, precision, recall, F2 score, and AUROC, computed after a stratified 70% training and 30% test split.","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},[93,97,101,105,110,115,120,123,128,131,135],{"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":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]