[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128530-en":3,"doc-seo-128530-105":31,"detail-sidebar-cat-0-en-105":92},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128530,687207020761,"Patrick","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","An In-Depth Comparative Analysis of Machine Learning Techniques for Addressing Class Imbalance in Mental Health Prediction","Machine learning applied to mental healthcare prediction is constrained by imbalanced datasets, where the majority of samples come from diagnosed conditions and minority classes are underrepresented. This imbalance can bias model training and reduce generalisation, limiting reliable prediction. The study compares multiple class-imbalance strategies, including resampling, ensemble learning, and algorithm-specific methods, evaluated with accuracy, precision, recall, and F1. Using data from Open Sourcing Mental Illness (2016–2021), ensemble methods—especially Random Forest—outperform alternatives, with additional metrics such as Kappa, balanced accuracy, and geometric mean to assess effectiveness. Results support earlier diagnosis and more personalised treatment planning.","Association for Information Systems  \nAIS Electronic Library (AISeL)  \n\n| ACIS 2023 Proceedings | Australasian (ACIS) |\n| --- | --- |\n| 12-2-2023\u003Cbr>An In-Depth Comparative Analysis of Machine Learning Techniques for Addressing Class Imbalance in Mental Health Prediction.\u003Cbr>Tsholofelo Mokheleli\u003Cbr>University of Johannesburg, South Africa, [mokhelelitsholo48@gmail.com](mokhelelitsholo48@gmail.com)\u003Cbr>Tebogo Bokaba\u003Cbr>University of Johannesburg, South Africa, [tbokaba@uj.ac.za](tbokaba@uj.ac.za)\u003Cbr>Tinofirei Museba\u003Cbr>University of Johannesburg, South Africa, [tmuseba@uj.ac.za](tmuseba@uj.ac.za)\u003Cbr>Follow this and additional works at: [https://aisel.aisnet.org/acis2023](https://aisel.aisnet.org/acis2023) |  |\n\nRecommended Citation  \nMokheleli, Tsholofelo; Bokaba, Tebogo; and Museba, Tinofirei, \"An In-Depth Comparative Analysis of Machine Learning Techniques for Addressing Class Imbalance in Mental Health Prediction.\" (2023) . ACIS 2023 Proceedings. 15.  \n[https://aisel.aisnet.org/acis2023/15](https://aisel.aisnet.org/acis2023/15)  \nThis material is brought to you by the Australasian (ACIS) at AIS Electronic Library (AISeL) . It has been accepted for inclusion in ACIS 2023 Proceedings by an authorized administrator of AIS Electronic Library (AISeL) . For more information, please [contact](contact elibrary@aisnet.org)[ elibrary@aisnet.org](contact elibrary@aisnet.org).  \nAn In-Depth Comparative Analysis of Machine Learning Techniques for Addressing Class Imbalance in Mental Health Prediction  \nFull research paper  \nTsholofelo Mokheleli  \nDepartment of Applied Information Systems University of Johannesburg Johannesburg, South Africa [Email: mokhelelitsholo48@gmail.com](Email: mokhelelitsholo48@gmail.com)  \nTebogo Bokaba  \nDepartment of Applied Information Systems University of Johannesburg Johannesburg, South Africa  \nEmail: [tbokaba@uj.ac.za](tbokaba@uj.ac.za)  \nTinofirei Museba  \nDepartment of Applied Information Systems University of Johannesburg Johannesburg, South Africa  \nEmail: [tmuseba@uj.ac.za](tmuseba@uj.ac.za)  \nAbstract  \nThe application of machine learning (ML) in predicting mental healthcare faces a challenge due to imbalanced datasets. ML techniques analyse extensive datasets to make predictions; however, the unequal distribution of samples, with the majority belonging to diagnosed mental disorders, can lead to biased model training and limited generalisation. To mitigate the issue of class imbalance in mental health datasets, this study employed diverse ML techniques, namely, resampling, ensemble, and algorithm-specific approaches and metrics such as accuracy, precision, recall and F1 score. The dataset used was collected from the Open Sourcing Mental Illness website, spanning 2016 to 2021. The findings indicate that ensemble techniques, particularly Random Forest, excelled in managing class imbalance compared to other methods. Beyond conventional performance metrics, the study introduced Kappa, balanced accuracy, and geometric mean to evaluate model effectiveness. These findings provide valuable insights for improving mental health predictions, enabling early diagnosis and personalised treatment strategies.  \nKeywords Class imbalance, Cross-validation, Machine learning, Mental health prediction, Resampling methods.  \n1 Introduction  \nIn recent years, the application of machine learning (ML) for mental health prediction has gained increasing attention, driven by its potential to facilitate the early detection of mental disorders (Awal et al., 2021) . ML algorithms have demonstrated remarkable capabilities in analysing vast datasets encompassing medical records, physiological measurements, and patient-reported symptoms, enabling the prediction of the likelihood of developing mental health disorders and evaluating the efficacy of various treatment options (Vahdat et al. 2016). Accurately predicting mental health conditions can playa significant role in timely intervention, personalised treatment planning, and improve","cbCaig3cUOlCnQnt","https://ap.wps.com/l/cbCaig3cUOlCnQnt","pdf",447431,3,1,12,"English","en",105,"# Introduction\n## Background and motivation\n## Class imbalance problem\n# Study aim and contributions\n## Comparative evaluation of techniques\n# Methodology and evaluation\n## Resampling, ensemble, and algorithm-specific approaches\n## Performance metrics","[{\"question\":\"Why does class imbalance affect machine learning for mental health prediction?\",\"answer\":\"Imbalanced datasets have disproportionate sample distributions across disorders, causing biased training and weaker generalisation. Underrepresented disorders may be harder for models to recognise and differentiate accurately.\"},{\"question\":\"Which machine learning approaches were used to address class imbalance in the study?\",\"answer\":\"The study applied resampling, ensemble techniques, and algorithm-specific approaches. It also assessed performance using multiple evaluation metrics.\"},{\"question\":\"What were the main findings and which method performed best?\",\"answer\":\"Ensemble techniques, particularly Random Forest, performed best for managing class imbalance. The paper also used metrics beyond accuracy, including Kappa, balanced accuracy, and geometric mean, to confirm model effectiveness.\"}]","An In-Depth Comparative Analysis of Machine Learning Techniques for Addressing Class Imbalance in Mental Health Prediction | PDF",1786001584,30,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"an-in-depth-comparative-analysis-of-machine-learning-techniques-for-addressing-class-imbalance-in-mental-health-prediction","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/document/research-report/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/an-in-depth-comparative-analysis-of-machine-learning-techniques-for-addressing-class-imbalance-in-mental-health-prediction/128530/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-24","2026-08-06",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why does class imbalance affect machine learning for mental health prediction?","Question",{"text":76,"@type":77},"Imbalanced datasets have disproportionate sample distributions across disorders, causing biased training and weaker generalisation. Underrepresented disorders may be harder for models to recognise and differentiate accurately.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which machine learning approaches were used to address class imbalance in the study?",{"text":81,"@type":77},"The study applied resampling, ensemble techniques, and algorithm-specific approaches. It also assessed performance using multiple evaluation metrics.",{"name":83,"@type":74,"acceptedAnswer":84},"What were the main findings and which method performed best?",{"text":85,"@type":77},"Ensemble techniques, particularly Random Forest, performed best for managing class imbalance. The paper also used metrics beyond accuracy, including Kappa, balanced accuracy, and geometric mean, to confirm model effectiveness.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,123,128,131,135],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":30,"slug":122},"research-report",{"id":124,"doc_module":4,"doc_module_name":47,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":47,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":47,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]