[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118230-en":3,"doc-seo-118230-105":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":4,"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},118230,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Assessing the Reliability of Machine Learning Models Applied to the Mental Health Domain Using Explainable AI","Machine learning is increasingly applied in healthcare, but standard metrics such as accuracy, precision, and recall do not guarantee that model outcomes are reliable. This study evaluates multiple machine learning algorithms on a mental-health dataset using explainable AI methods. By combining model explainability with comparative assessment, the work examines how predictions are formed and whether feature importance is sound. Findings show reliance on less relevant features and occasionally unsound feature ranking.","San Jose State University  \nSJSU ScholarWorks  \nFaculty Research, Scholarly, and Creative Activity  \n3-1-2024  \nAssessing the Reliability of Machine Learning Models Applied to the Mental Health Domain Using Explainable AI  \nVishnu Pendyala  \nSan Jose State University, [vishnu.pendyala@sjsu.edu](vishnu.pendyala@sjsu.edu)  \nHyungkyun Kim  \nSan Jose State University  \nFollow this and additional works at: [https://scholarworks.sjsu.edu/faculty_rsca](https://scholarworks.sjsu.edu/faculty_rsca)  \nRecommended Citation  \nVishnu Pendyala and Hyungkyun Kim. \"Assessing the Reliability of Machine Learning Models Applied to the Mental Health Domain Using Explainable AI\" Electronics (Switzerland) (2024) . [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/electronics13061025](10.3390/electronics13061025)  \nThis Article is brought to you for free and open access by SJSU ScholarWorks. It has been accepted for inclusion in Faculty Research, Scholarly, and Creative Activity by an authorized administrator of SJSU ScholarWorks. For more information, please contact [scholarworks@sjsu.edu](scholarworks@sjsu.edu).  \n electronics   \nArticle  \nAssessing the Reliability of Machine Learning Models  \nApplied to the Mental Health Domain Using Explainable AI  \nVishnu Pendyala 1, *,† and Hyungkyun Kim 2,†  \nCitation: Pendyala, V.; Kim, H. Assessing the Reliability of Machine Learning Models Applied to the Mental Health Domain Using Explainable AI. Electronics 2024, 13, 1025. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)electronics13061025  \nAcademic Editor: William M. Mongan  \nReceived: 31 December 2023  \nRevised: 26 February 2024  \nAccepted: 4 March 2024  \nPublished: 8 March 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Applied Data Science, San Jose State University, San Jose, CA 95192, USA  \n2 Department of Computer Science, San Jose State University, San Jose, CA 95192, USA; [hyungkyun.kim@sjsu.edu](hyungkyun.kim@sjsu.edu)  \n* [Correspondence: vishnu.pendyala@sjsu.edu](Correspondence: vishnu.pendyala@sjsu.edu)[ ](Correspondence: vishnu.pendyala@sjsu.edu)† These authors contributed equally to this work.  \nAbstract: Machine learning is increasingly and ubiquitously being used in the medical domain. Evaluation metrics like accuracy, precision, and recall may indicate the performance of the models but not necessarily the reliability of their outcomes. This paper assesses the effectiveness of a number of machine learning algorithms applied to an important dataset in the medical domain, specifically, mental health, by employing explainability methodologies. Using multiple machine learning algorithms and model explainability techniques, this work provides insights into the models’workings to help determine the reliability of the machine learning algorithm predictions. The results are not intuitive. It was found that the models were focusing significantly on less relevant features and, at times, unsound ranking of the features to make the predictions. This paper therefore argues that it is important for research in applied machine learning to provide insights into the explainability of models in addition to other performance metrics like accuracy. This is particularly important for applications in critical domains such as healthcare.  \nKeywords: explainable AI; machine learning; mental health; model evaluation  \n1. Introduction  \nMental health is a serious issue. For the past few years, a non-profit organization, Open Sourcing Mental Health (OSMH), has been publishing the results of its survey on the mental health of employees primarily in the tech/IT industry under Creative Commo","cbCaiuemh52uEPBu","https://ap.wps.com/l/cbCaiuemh52uEPBu","pdf",2627410,1,22,"English","en",105,"# Introduction\n## Research Questions\n# Literature Review","[{\"question\":\"Why are traditional evaluation metrics not enough to judge reliability in mental-health machine learning models?\",\"answer\":\"Accuracy, precision, and recall can reflect performance without ensuring the reliability of outcomes. The study argues that models may look effective while basing predictions on unreliable or irrelevant feature signals.\"},{\"question\":\"What role do LIME and SHAP play in this paper?\",\"answer\":\"LIME and SHAP are used to enhance transparency by explaining individual predictions. The experiments test whether these explanations reveal dependable reasoning behind the model outputs.\"},{\"question\":\"What did the reliability analysis reveal about feature usage in the models?\",\"answer\":\"Model predictions relied on unsound weights for dataset features and were sometimes driven by less relevant features. The feature ranking produced by explanations was not always intuitive or justified.\"}]","Assessing the Reliability of Machine Learning Models Applied to the Mental Health Domain Using Explainable AI | PDF",1785682454,55,{"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},"assessing-the-reliability-of-machine-learning-models-applied-to-the-mental-health-domain-using-explainable-ai","",{"@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/assessing-the-reliability-of-machine-learning-models-applied-to-the-mental-health-domain-using-explainable-ai/118230/",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","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why are traditional evaluation metrics not enough to judge reliability in mental-health machine learning models?","Question",{"text":75,"@type":76},"Accuracy, precision, and recall can reflect performance without ensuring the reliability of outcomes. The study argues that models may look effective while basing predictions on unreliable or irrelevant feature signals.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What role do LIME and SHAP play in this paper?",{"text":80,"@type":76},"LIME and SHAP are used to enhance transparency by explaining individual predictions. The experiments test whether these explanations reveal dependable reasoning behind the model outputs.",{"name":82,"@type":73,"acceptedAnswer":83},"What did the reliability analysis reveal about feature usage in the models?",{"text":84,"@type":76},"Model predictions relied on unsound weights for dataset features and were sometimes driven by less relevant features. 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