[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121289-en":3,"doc-seo-121289-105":29,"detail-sidebar-cat-0-en-105":90},{"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":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},121289,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Uncovering Acoustic Biomarkers to Classify Parkinson Disease through Machine Learning","Early detection directly impacts treatment efficacy, making dependable classification methods vital for accurate Parkinson’s disease identification. This project compares five classifiers—Logistic Regression, Gaussian Naive Bayes, K Nearest Neighbor, Extreme Gradient Boosting, and Support Vector Machines—using voice features. Results show XGBoost achieving the highest accuracy of 91% on the full dataset. After variable selection, KNN reaches 91% accuracy, indicating machine learning can reveal actionable insights for disease detection.","University of Central Florida  \nSTARS  \nData Science and Data Mining  \nJanuary 2025  \nUncovering Acoustic Biomarkers to Classify Parkinson Disease through Machine Learning  \nFelix Yeboah  \nUniversity of Central Florida, [fe528610@ucf.edu](fe528610@ucf.edu)  \n Part of the Analytical, Diagnostic and Therapeutic Techniques and Equipment Commons, and the Data Science Commons  \nFind similar works at: [https://stars.library.ucf.edu/data-science-mining](https://stars.library.ucf.edu/data-science-mining)  \nUniversity of Central Florida Libraries [http://library.ucf.edu](http://library.ucf.edu)  \nThis Article is brought to you for free and open access by STARS. It has been accepted for inclusion in Data Science and Data Mining by an authorized administrator of STARS. For more information, [please contact STARS@ucf.edu](please contact STARS@ucf.edu).  \nSTARS Citation  \nYeboah, Felix, \"Uncovering Acoustic Biomarkers to Classify Parkinson Disease through Machine Learning\" (2025) . Data Science and Data Mining. 34.  \n[https://stars.library.ucf.edu/data-science-mining/34](https://stars.library.ucf.edu/data-science-mining/34)  \nUncovering Acoustic Biomarkers to Classify Parkinson Disease through Machine Learning  \nFelix Yeboah  \nDepartment of Statistics and Data Science  \nUniversity of Central Florida  \nOrlando, United States  \n[fe528610@ucf.edu](fe528610@ucf.edu)  \nAbstract—The early detection of diseases profoundly infuences treatment effcacy, and accurate classifcation methodologies are essential for effective disease identifcation. In this project, we examined fve different classifers—Logistic Regression, Gaussian Naive Bayes, K Nearest Neighbor (KNN), Extreme Gradient Boosting (XGBoost), and Support Vector Machines—and evaluated their performance in detecting Parkinson’s disease (PD) based on voice features. The study aims to identify the best classifer for detecting PD. XGBoost performed the best with an accuracy of 91% on the full dataset. After variable selection, KNN had the best performance with an accuracy of 91% . These fndings suggest that Machine learning algorithms(classifers) can offer valuable insights into disease detection.  \nIndex Terms—Parkinson’s disease, Logistic Regression, Gaussian Naive Bayes, K Nearest Neighbor (KNN), Extreme Gradient Boosting (XGBoost), Support Vector Machines  \nI. INTRODUCTION  \nArguably, after Alzheimer’s, Parkinson’s disease(PD) is the world’s second most occurring neurodegenerative disorder. PD is a recurring and progressive neurologic disorder that results from cell loss in the substantia nigra –the brain area responsible for producing dopamine. It is characterized by tremors, stiffness of the muscles, bradykinesia –slow movement, and other subtle symptoms such as depression and cognitive impairment, among many others [1] . Parkinson’s disease (PD) is currently the most rapidly expanding neurological disorder worldwide in terms of disability, mortality, and age-adjusted prevalence. According to 2019 data from the World Health Organization (WHO), the global number of individuals with PD surpassed 8.5 million, marking a signifcant increase from 6.1 million cases in 2016 and 2.5 million in 1990. By 2040, the global burden of PD is projected to exceed 17 million cases [2] .  \nCurrently, there are no defnitive diagnostic tests or reliable biomarkers available for the diagnosis of Parkinson’s disease (PD) . Many other neurodegenerative disorders, such as essential tremor and Lewy body dementia, have symptoms similar to Parkinson’s disease, making the confrmation of PD very diffcult. PD diagnosis often requires the exclusion of other disorders with Parkinson-like symptoms. Despite knowing some noticeable symptoms, neurologists and clinicians primarily identify PD after substantial loss of dopamine neurons. Early diagnosis remains a challenge due to the overlap of clinical features of PD with other neurodegenerative conditions, mostly  \nleading to missed or misdiagnosed cases.  \nDebatably, every 1 ou","cbCaioxzIQ6ZaFQn","https://ap.wps.com/l/cbCaioxzIQ6ZaFQn","pdf",1089065,1,"English","en",105,"# Introduction\n## Parkinson’s disease burden and diagnosis challenges\n## Speech impairments as diagnostic clues\n## Motivation for machine learning and acoustic analysis","[{\"question\":\"What is the main goal of this project?\",\"answer\":\"To identify acoustic biomarkers from voice features and determine the best machine learning classifier for detecting Parkinson’s disease (PD).\"},{\"question\":\"Which classifiers are evaluated in the study?\",\"answer\":\"Logistic Regression, Gaussian Naive Bayes, K Nearest Neighbor (KNN), Extreme Gradient Boosting (XGBoost), and Support Vector Machines are compared.\"},{\"question\":\"What performance results does the study report?\",\"answer\":\"XGBoost achieves 91% accuracy on the full dataset. After variable selection, KNN also reaches 91% accuracy.\"}]","Uncovering Acoustic Biomarkers to Classify Parkinson Disease through Machine Learning | PDF",1785734921,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"uncovering-acoustic-biomarkers-to-classify-parkinson-disease-through-machine-learning","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/uncovering-acoustic-biomarkers-to-classify-parkinson-disease-through-machine-learning/121289/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What is the main goal of this project?","Question",{"text":74,"@type":75},"To identify acoustic biomarkers from voice features and determine the best machine learning classifier for detecting Parkinson’s disease (PD).","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"Which classifiers are evaluated in the study?",{"text":79,"@type":75},"Logistic Regression, Gaussian Naive Bayes, K Nearest Neighbor (KNN), Extreme Gradient Boosting (XGBoost), and Support Vector Machines are compared.",{"name":81,"@type":72,"acceptedAnswer":82},"What performance results does the study report?",{"text":83,"@type":75},"XGBoost achieves 91% accuracy on the full dataset. 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