[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118628-en":3,"doc-seo-118628-105":30,"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":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},118628,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",8,"Research & Report","Predictive Modelling for Mental Health Disorder using Machine Learning Techniques - Jurnal Sarjana Teknik Informatika","Study evaluates how machine learning techniques improve prediction and diagnosis of mental health disorders, addressing limitations of traditional methods that are subjective and time-consuming. Using an Open-Sourcing Mental Illness survey dataset, five algorithms are compared: logistic regression, decision trees, random forests, k-nearest neighbours, and naïve bayes. Results show Naïve Bayes achieves the highest accuracy at 82.54%, indicating strong suitability for more reliable mental health diagnostics. Findings support earlier detection and improved management, and motivate future work using broader datasets and ensemble approaches for clinical use.","| Jurnal Sarjana Teknik Informatika | e-ISSN: 2809-3399, p-ISSN: 2338-5197 | 23 |\n| --- | --- | --- |\n| Vol. 13., No. 1, Februari 2025, pp. 23-32 | [http://journal.uad.ac.id/index.php/JSTIF/index](http://journal.uad.ac.id/index.php/JSTIF/index) |  |\n\nPredictive Modelling for Mental Health Disorder using Machine Learning Techniques  \nEmmanuel Nwelih a,1, Victor Osasu Eguavoen b,2,*  \na Department of Computer Science, University of Benin, Benin City, Edo State, Nigeria  \nbDepartment of Computing, College of Science & Computing, Wellspring University, Benin City, Edo State, Nigeria  \n[1](1 emmanuel.nwelih@uniben.edu)[ emmanuel.nwelih@uniben.edu](1 emmanuel.nwelih@uniben.edu);2,* [eguavoen.osasu@wellspringuniversity.edu.ng](eguavoen.osasu@wellspringuniversity.edu.ng)  \n* Penulis Korespondensi  \nABSTRAK  \n\n| This study evaluates the application of machine learning techniques in improving the prediction and diagnosis of mental health disorders. Traditional diagnostic methods are subjective and time-consuming, necessitating more accurate and efficient alternatives. Using a dataset from the Open-Sourcing Mental Illness survey, this study compares five machine learning algorithmslogistic regression, decision trees, random forests, k-nearest neighbours, andnaïve bayes-on mental health prediction tasks. The findings indicate that Naïve Bayes achieves the highest accuracy (82.54%), suggesting its potential for more accurate mental health diagnostics. These results underscore the value of machine learning techniques in enhancing early detection and management of mental health conditions, paving the way for future research into more diverse datasets and ensemble approaches to refine predictive models for clinical application. |  | Riwayat Artikel\u003Cbr>Diterima 21 Juli 2024\u003Cbr>Diperbaiki 5 Desember 2024 Diterbitkan 25 Februari 2025 |\n| --- | --- | --- |\n|  |  | Kata Kunci\u003Cbr>Bipolar Disorder Prediction Depression Detection Machine Learning\u003Cbr>Mental Health Disorder Stess Detection |\n\nThis is an open-access article under the CC–BY-SA license  \n1. Introduction  \nMental health issues span a wide array of conditions affecting an individual ’s thoughts, emotions, behaviour, and overall well-being. According to the World Health Organization, approximately 1 in 8 people will experience a mental health disorder at some point in their lives, highlighting the global urgency for effective mental health interventions [1] . Mental health conditions not only compromise individual well-being but also impose significant social and economic burdens, including decreased workplace productivity, strained interpersonal relationships, and a diminished quality of life [2] . Early detection of these conditions enables better treatment and improves the value of life [3, 4] . Mental stress significantly contributes to various psychological and physical diseases, such as ADHD, sleep apnea, and depression [5, 6, 7, 8] .  \nTraditional assessment methods, such as self-reports and clinical appraisals, are subjective, timeconsuming, and limited in scope [3] . In contrast, machine learning (ML) techniques provide a promising alternative for improving the accuracy, efficiency, and scalability of mental health diagnosis. Recent advancements in ML have enabled the use of large-scale datasets, facilitating more objective and automated tools for diagnosing mental health conditions like depression, anxiety, and stress [8] . Mental health significantly impacts quality of life, work capacity, and relationships. It impacts individuals’ reactions to stress, interactions with others, and decision-making processes.  \nThe utilization of ML in mental health diagnostics has demonstrated significant potential to improve predictive accuracy by identifying complex patterns within data that conventional assessment methods may overlook [4] . Currently, ML techniques are increasingly applied for the diagnosis, prognosis, and management of a wide spectrum of mental health conditions, including aut","cbCair3dyvce52Fr","https://ap.wps.com/l/cbCair3dyvce52Fr","pdf",953142,1,10,"English","en",105,"# Introduction\n# Literature Review","[{\"question\":\"What problem does the study address in mental health diagnosis?\",\"answer\":\"Traditional diagnostic approaches are subjective and time-consuming, creating a need for more accurate, efficient, and scalable prediction methods.\"},{\"question\":\"Which machine learning algorithms are compared in the study?\",\"answer\":\"The study compares logistic regression, decision trees, random forests, k-nearest neighbours, and naïve bayes for mental health prediction tasks.\"},{\"question\":\"What is the best-performing model and its reported accuracy?\",\"answer\":\"Naïve Bayes achieves the highest accuracy at 82.54%, indicating strong potential for improving mental health diagnostics.\"}]","Predictive Modelling for Mental Health Disorder using Machine Learning Techniques - 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