[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121451-en":3,"doc-seo-121451-105":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},121451,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",8,"Research & Report","Evaluation of Machine Learning-Based Methods to Detect Bipolar Disorder in Individuals With Mental Health Conditions - read online free","Bipolar disorder remains a highly debilitating neuroaffective condition characterized by destabilizing mood swings that disrupt interpersonal functioning and work stability. Early and dependable diagnosis supports timely pharmacological treatment and mental-health education, potentially reducing harm to patients and broader social systems. This study evaluates machine learning models—Random Forest, Support Vector Machine, and Gradient Boosting—for classification and prognostication using a clinical dataset of 120 participants across bipolar subtypes, major depressive disorder, and healthy controls.","Keywords: Mental Health, Random Forest, Support Vector Machine, Bipolar Disorder, Psychiatry.  \nJournal Info:  \nSubmitted: June 25, 2025  \nAccepted:  \nAugust 30, 2025  \nPublished:  \nSeptember 09, 2025  \nEvaluation of Machine Learning–Based Methods to Detect Bipolar Disorder in Individuals With Mental Health Conditions  \nSyed Ibad Hasnain  \n1*  \n,  \nHafsa Israr2 , Muhammad Faris 1 , Rabika Kamal  \n1  \n,  \nHafiza  \nSyeda Yusra Tirmizi  \n1  \n1 Faculty of Engineering Science and Technology, Hamdard University, Karachi 74600, Pakistan ;  \n2 Department of Biomedical Engineering, Sir Syed University of Engineering, Karachi, Pakistan  \nAbstract  \nBipolar disorder (BD) is still one of the most incapacitating of neuroaffective disorders in psychiatry. The strong mood swings from states of euphoria to depression often destabilize interpersonal relationships and can undo occupational stability. Early and reliable diagnosis facilitates prompt pharmacological intervention and mental-health education that may protect not only the patient and their immediate social circle but also the entire social structure from general distress. In this research study the performance of machine learning algorithms such as random forest (RF), support vector machine (SVM) and gradient boosting (GB) has been investigated for classification and prognostication of BD and its subtypes. The machine learning models were validated using a clinical dataset, which included 120 participants: 28 of BD I, 31 of BD II, 31 of Major Depressive Disorder and 30 healthy controls. Model performance was evaluated with stratified cross-validated train-test-split and a set of metrics, including accuracy, precision, recall, F1-score, and Receiver Operating Characteristic-Area under the Curve (ROC vs. AUC) . In other words, the RF model had the highest accuracy (88%), precision (90%), and recall (88%) . The discriminative performance of RF and SVM models was comparable with an ROC-AUC of 97% . These results emphasize the potential of machine learning (ML), specifically ensemble techniques like Random Forest (RF), as an effective supplement to traditional early clinical diagnosis in bipolar disorders and related psychiatric illnesses.  \n*Correspondence author email address: [ibad.hasnain@hamdard.edu.pk](ibad.hasnain@hamdard.edu.pk)[ ](ibad.hasnain@hamdard.edu.pk)DOI: 10.21015/vtse.v13i3 .2173  \n1 Introduction  \nBipolar disorder (BD) is clinically defined by a pattern of alternating euthymia, mania, depression and some-  \ntimes mixed states; the profound mood fluctuationshave brought BD to a level of public-health concern [1, 2] . Epidemiological surveys have suggested that ap-  \nThis work is licensed under a Creative Commons Attribution 3.0 License.  \nVFAST Transactions on Software Engineering Volume 13, Issue 3, 2025  \nproximately one-percent of the world’s population is affected by the disorder with prevalence rates remaining constant across different cultural settings [3] .  \nFormally, bipolar I is characterized by fully formed manic episodes while bipolar II is characterized by recurrent depressive episodes with hypomania of a lesser intensity [4] . Subtypes of patients show marked deficits in interpersonal relationships, occupational performance and fundamental executive functioning, and the risk of suicidality is elevated by an approximate twenty to thirty-fold compared to non-affected populations [5] .  \nIn clinical practise, BD is only exceptionally found in isolation; co-morbid expressions such as emotional dysregulation, depression and anxiety make diagnostic clarification and pharmacological therapy even more complicated [6] . Economic analyses suggest that direct medical costs are high, but that indirect costs (expressed as lost wages and reduction in production output) typically minimizes these direct costs [7] . With high prevalence, shortened life expectancy and substantial economic cost, research however remain on track looking for more effective algorithms of treatment, new ","cbCailOvfrLh4VUc","https://ap.wps.com/l/cbCailOvfrLh4VUc","pdf",314802,1,11,"English","en",105,"# Abstract\n# Introduction\n## Clinical background and public-health impact\n## Subtypes, comorbidity, and economic burden\n## Etiology, biomarkers, and diagnostic workflow\n# Methods (information not fully available in provided text)","[{\"question\":\"Why is early diagnosis of bipolar disorder important?\",\"answer\":\"Early and reliable diagnosis enables prompt pharmacological intervention and mental-health education, helping protect patients and their immediate social environment and reducing broader distress.\"},{\"question\":\"Which machine learning algorithms are evaluated for bipolar disorder detection?\",\"answer\":\"The study investigates Random Forest, Support Vector Machine, and Gradient Boosting for classification and prognostication, validated on a clinical dataset.\"},{\"question\":\"How was model performance assessed in the study?\",\"answer\":\"Models were evaluated using stratified cross-validated train-test splitting and metrics including accuracy, precision, recall, F1-score, and ROC-AUC.\"}]","Evaluation of Machine Learning-Based Methods to Detect Bipolar Disorder in Individuals With Mental Health Conditions - 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