[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125520-en":3,"doc-seo-125520-105":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},125520,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",8,"Research & Report","Mental Health Problems Prediction Using Machine Learning Techniques - Random Forest","Mental health problems include conditions that affect emotions and behaviors, and traditional prediction approaches often lead to over-detection or under-detection while requiring time-consuming manual review during screening. This research develops machine learning models to support clinical screening and diagnosis by predicting mental health problems from survey factors. Logistic Regression, K-Nearest Neighbors, and Random Forest are trained using Open Source Mental Disorders data (2014) with feature selection and hyperparameter fine-tuning, then evaluated via accuracy, recall, precision, F1 score, and AUROC.","International Journal on Robotics, Automation and Sciences  \nMental Health Problems Prediction Using Machine Learning  \nTechniques  \nJia-Pao Cheng, Su-Cheng Haw*  \nAbstract-Mental health problems encompass a range of conditions that can impact an individual's emotions and behaviors. The conventional methods of mental illness prediction often suffer from the issue of either over-detection or under-detection and the timeconsuming manual review process of patients' data during screening sessions. Therefore, this research aims to utilize machine learning techniques to predict mental health problems, complementing the traditional clinical screening and diagnosis process. The proposed models in this project: Logistic Regression, K-Nearest Neighbors, and Random Forest leverage relevant factors from the dataset concerning mental health survey published by Open Source Mental Disorders in 2014 to predict mental health problems. Feature selection and hyperparameter fine-tuning are employed to identify the factors contributing to mental health problems and enhance the performance of the models. The evaluation of these models is measured using accuracy, recall, precision, F1 score, and AUROC. Experimental evaluation results indicated that the Random Forest model utilizing hyperparameters derived from the RandomizedSearchCV method outperforms during model selection using crossvalidation. When predicting test set data, it exhibits a good generalization with an accuracy of 83.23%, recall of 89.87%, precision of 78.02%, F1 score of 83.53%, and AUROC of 83.57% .  \nKeywords—Mental Health Problems, Logistic Regression, KNearest Neighbors, Random Forest.  \nI. INTRODUCTION  \nMental health problems are conditions that may affect a person's feelings and behavior, resulting in psychosocial impairments or loss of ability which require treatment [1] . The World Health Organization reported that in 2019, among the population of 970 million people, there was 1 in every 8 people suffered from a mental disorder [2] . One in three Malaysians, according to the National Health and Morbidity Survey conducted by the Ministry of Health [3], struggles with mental health issues, with the highest prevalence in teenagers aged 16 to 19 and from low-income families. In truth, there are numerous mental illnesses for which the majority of individuals do not have access to the necessary care, including anxiety, depression, post-traumatic stress disorder, and bipolar disorder due to reasons such as lack of knowledge, ignorance in treatment access , and more [4] . A person's social interactions and daily activities might be disrupted by mental health issues, which can also lead to poor work performance. In the worst-case scenario, self-harm and suicide may also emerge [5] .  \n*Corresponding author, Email: [sucheng@mmu.edu.my](sucheng@mmu.edu.my) , ORCID: 0000-0002-7190-0837  \nSu-Cheng Haw is a professor under Faculty of Computing and Informatics, Multimedia University, Persiaran Multimedia 63100, Cyberjaya, Malaysia (email: [sucheng@mmu.edu.my](sucheng@mmu.edu.my)) .  \nJia-Pao Cheng is a student under Faculty of Computing and Informatics, Multimedia University, Persiaran Multimedia 63100, Cyberjaya, Malaysia (1[191101533@student.mmu.edu.my](191101533@student.mmu.edu.my)) .  \nInternational Journal on Robotics, Automation and Sciences (2023) 5,2:59-72  \n[https://doi](https://doi)  \nTo emphasize, mental health diagnosis is a complex process that is not straightforward. Traditional methods of predicting mental health entail a series of clinical screenings that include a face-to-face interview between a patient and a human doctor, completing questionnaires, and taking psychological tests. The process is prone to misdiagnosis, especially with a higher number of false positive cases [6] . Moreover, manually reviewing patient data for decision-making is ineffective and time-consuming, leading to a delay in early diagnosis in which early intervention can bring positive impacts on me","cbCaisKGsSPzNWxN","https://ap.wps.com/l/cbCaisKGsSPzNWxN","pdf",813799,1,14,"English","en",105,"# Abstract\n# I. Introduction\n# II. Background and Related Work\n## A. Overview of Machine Learning","[{\"question\":\"Why are traditional mental health prediction methods inadequate?\",\"answer\":\"They can cause over-detection or under-detection and often rely on time-consuming manual review of patient data during screening sessions, increasing delays in decision-making.\"},{\"question\":\"Which machine learning models are used to predict mental health problems?\",\"answer\":\"The study uses Logistic Regression, K-Nearest Neighbors, and Random Forest to predict whether an individual has mental health problems.\"},{\"question\":\"How are the models optimized and evaluated?\",\"answer\":\"Feature selection and hyperparameter fine-tuning are applied to improve performance, and results are measured using accuracy, recall, precision, F1 score, and AUROC.\"}]","Mental Health Problems Prediction Using Machine Learning Techniques - Random Forest | PDF",1785899590,35,{"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},"mental-health-problems-prediction-using-machine-learning-techniques-random-forest","",{"@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/mental-health-problems-prediction-using-machine-learning-techniques-random-forest/125520/",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},"Why are traditional mental health prediction methods inadequate?","Question",{"text":75,"@type":76},"They can cause over-detection or under-detection and often rely on time-consuming manual review of patient data during screening sessions, increasing delays in decision-making.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning models are used to predict mental health problems?",{"text":80,"@type":76},"The study uses Logistic Regression, K-Nearest Neighbors, and Random Forest to predict whether an individual has mental health problems.",{"name":82,"@type":73,"acceptedAnswer":83},"How are the models optimized and evaluated?",{"text":84,"@type":76},"Feature selection and hyperparameter fine-tuning are applied to improve performance, and results are measured using accuracy, recall, precision, F1 score, and AUROC.","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"]