[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-290795-105":59,"doc-detail-290795-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","modelling-tinnitus-functional-index-reduction-using-supervised-machine-learning-algorithms","Modelling Tinnitus Functional Index reduction using supervised machine learning algorithms","","This study models the reduction in the Tinnitus Functional Index (TFI) using supervised machine learning algorithms, primarily Ordinary Least Squares (OLS), K-Nearest Neighbor (KNN), Ridge, and Lasso regressions. Group, ISI, and SWLS are identified as significant predictors of TFI reduction via best subset selection and supported by forward and backward selection in OLS. Ridge and Lasso outperform OLS and KNN, with Ridge achieving the smallest test mean square error (318.30). Ridge is selected as the best model, motivating supervised learning for more precise personalized Tinnitus treatment and improved quality of life, while future work should extend to multimodal and longitudinal data.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/modelling-tinnitus-functional-index-reduction-using-supervised-machine-learning-algorithms/290795/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/modelling-tinnitus-functional-index-reduction-using-supervised-machine-learning-algorithms/290795.png","ImageObject",300,407,{"name":92,"@type":93},"River Wang","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",14,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"Which supervised machine learning methods are used to model TFI reduction?","Question",{"text":113,"@type":114},"The study uses Ordinary Least Squares (OLS), K-Nearest Neighbor (KNN), Ridge, and Lasso regressions to model Tinnitus Functional Index (TFI) reduction.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"What predictors are found to be significant for TFI reduction?",{"text":118,"@type":114},"Group, ISI, and SWLS are highlighted as significant predictors of TFI reduction, identified through best subset selection and validated with forward and backward selection criteria in the OLS regression.",{"name":120,"@type":111,"acceptedAnswer":121},"Why is Ridge regression considered the best-performing model in the study?",{"text":122,"@type":114},"Ridge regression shows superior performance to OLS and KNN, achieving the smallest test mean square error (318.30), lower than Lasso (319.28), OLS (330.76), and KNN (584.92).","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},290795,1789642579,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":145,"preview_page_count":4},1099514067438,"https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542","University of Texas Rio Grande Valley  \nScholarWorks @ UTRGV  \nSchool of Mathematical and Statistical  \nSciences Faculty Publications and College of Sciences  \nPresentations  \n12-2024  \nModelling Tinnitus Functional Index reduction using supervised machine learning algorithms  \nEdmund F. Agyemang  \nThe University of Texas Rio Grande Valley, [edmund.agyemang01@utrgv.edu](edmund.agyemang01@utrgv.edu)  \nFollow this and additional works at: [https://scholarworks.utrgv.edu/mss_fac](https://scholarworks.utrgv.edu/mss_fac)  \n Part of the Mathematics Commons, and the Medicine and Health Sciences Commons  \nRecommended Citation  \nAgyemang, Edmund Fosu. \"Modelling Tinnitus Functional Index reduction using supervised machine learning algorithms.\" Statistics in Transition. New Series 25, no. 4 (2024): 51-77 . [https://doi.org/](https://doi.org/)[ ](https://doi.org/)10.59139/stattrans-2024-003  \nThis Article is brought to you for free and open access by the College of Sciences at ScholarWorks @ UTRGV. It has been accepted for inclusion in School of Mathematical and Statistical Sciences Faculty Publications and Presentations by an authorized administrator of ScholarWorks @ UTRGV. For more information, please contact [william.flores01@utrgv.edu](william.flores01@utrgv.edu).  \nSTATISTICS IN TRANSITION new series, December 2024  \nVol. 25, No. 4 pp. 51–77, [https://doi.org/10.59139/stattrans-2024-003](https://doi.org/10.59139/stattrans-2024-003)[ ](https://doi.org/10.59139/stattrans-2024-003)[Received](Received) – 26.03.2024; accepted – 17.07.2024  \nModelling Tinnitus Functional Index reduction using supervised machine learning algorithms  \nEdmund Fosu Agyemang 1  \nAbstract  \nThis study aims to model the reduction in the Tinnitus Functional Index (TFI) utilizing supervised machine learning algorithms, focusing primarily on Ordinary Least Squares (OLS), K-Nearest Neighbor (KNN), Ridge, and Lasso regressions. Our analysis highlighted Group, ISI, and SWLS as significant predictors ofTFI reduction, identified through the best subset selection and confirmed by both forward and backward selection criteria in the OLS regression. Notably, the shrinkage methods, Ridge and Lasso regressions, demonstrated superior performance compared to OLS and KNN, with the Ridge regression presenting the smallest test mean square error (MSE) of 318.30. This finding establishes the Ridge regression as the best model for analyzing our Tinnitus dataset relative to the other methods, which exhibited test MSEs of 319.28 (Lasso), 330.76 (OLS), and 584.92 (KNN), respectively. This research highlights the potential of supervised machine learning algorithms in advancing personalized Tinnitus treatment, reflecting broader trends in the field as evidenced by studies in the literature. By leveraging these algorithms, we can enhance treatment precision and outcomes, contributing significantly to improved quality of life for individuals with Tinnitus. Future research should explore the integration of multimodal data and longitudinal applications of these algorithms to further refine predictive capabilities and treatment effectiveness.  \nKey words: Tinnitus, K-Nearest Neighbor regression, Ridge regression, Lasso regression, multiple linear regression.  \n1. Introduction  \nTinnitus, characterized by the perception of noise or ringing in the ears in the absence of any external sound, is a prevalent and often distressing auditory phenomenon (De Ridder et al., 2021). Affecting a significant portion of the population,  \n1 Corresponding author. School of Mathematical and Statistical Science, College of Sciences, University of Texas Rio Grande Valley, USA &Department of Statistics and Actuarial Science, College of Basic and Applied Sciences, University of Ghana, Ghana &Department of Computer Science, Ashesi University, No. 1 University Avenue,  \nBerekuso-Ghana. E-mail: [edmundfosu6@gmail.com](edmundfosu6@gmail.com), ORCID: [https://orcid.org/0000-0001-8124-4493](https://orcid.org/0000-0001-81","cbCailQAWGSMWfWi","https://ap.wps.com/l/cbCailQAWGSMWfWi","pdf",2146479,28,"English","# Abstract\n# 1. Introduction","[{\"question\":\"Which supervised machine learning methods are used to model TFI reduction?\",\"answer\":\"The study uses Ordinary Least Squares (OLS), K-Nearest Neighbor (KNN), Ridge, and Lasso regressions to model Tinnitus Functional Index (TFI) reduction.\"},{\"question\":\"What predictors are found to be significant for TFI reduction?\",\"answer\":\"Group, ISI, and SWLS are highlighted as significant predictors of TFI reduction, identified through best subset selection and validated with forward and backward selection criteria in the OLS regression.\"},{\"question\":\"Why is Ridge regression considered the best-performing model in the study?\",\"answer\":\"Ridge regression shows superior performance to OLS and KNN, achieving the smallest test mean square error (318.30), lower than Lasso (319.28), OLS (330.76), and KNN (584.92).\"}]","Modelling Tinnitus Functional Index reduction using supervised machine learning algorithms | PDF",71]