[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120963-en":3,"doc-seo-120963-105":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":20,"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},120963,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Parkinson's Disease Detection through Vocal Biomarkers and Advanced Machine Learning Algorithms - Research Article","Parkinson’s disease (PD) is a common neurodegenerative disorder affecting motor neurons, producing tremors, stiffness, and gait difficulties. The study investigates whether vocal feature alterations can enable earlier prediction of PD onset. A range of advanced machine-learning models—including XGBoost, LightGBM, Bagging, AdaBoost, and Support Vector Machine—are compared using accuracy, AUC, sensitivity, and specificity. Results identify LightGBM as the top performer, reaching 96% accuracy with 96% AUC, 100% sensitivity, and 94.43% specificity. The findings support vocal biomarkers combined with machine learning for timely PD detection.","Journal of Computer Science and Technology Studies  \nISSN: 2709-104X  \nDOI: 10. 32996/jcsts  \nJournal [Homepage: www.al-kindipublisher.com/index.php/jcsts](Homepage: www.al-kindipublisher.com/index.php/jcsts)  \nJCSTS  \nAL-KINDI CENTER FOR RESEARCH AND DEVELOPMENT  \n\n| RESEARCH ARTICLE\u003Cbr>Parkinson's Disease Detection through Vocal Biomarkers and Advanced Machine Learning Algorithms\u003Cbr>Md Abu Sayed1 ✉ Maliha Tayaba2, MD Tanvir Islam3, Md Eyasin Ul Islam Pavel4, Md Tuhin Mia5, Eftekhar Hossain Ayon6, Nur Nob7 and Bishnu Padh Ghosh8\u003Cbr>1Department of Professional Security Studies, New Jersey City University, Jersey City, New Jersey, USA\u003Cbr>2Department of Computer Science, University of South Dakota, Vermillion, South Dakota, USA\u003Cbr>3Department of Computer Science, Monroe College, New Rochelle, New York, USA\u003Cbr>4Department of Public and Nonprofit Management, University of Texas at Dallas, Dallas, TX, USA\u003Cbr>5School of Business, International American University, Angeles, California, USA\u003Cbr>6Department of Computer & Info Science, Gannon University, Erie, Pennsylvania, USA\u003Cbr>7Department of Healthcare Management, Saint Francis College, Brooklyn, New York, USA.\u003Cbr>8School of Business, International American University, Los Angeles, California, USA\u003Cbr>Corresponding Author: Md Abu Sayed, [E-mail](E-mail: msayed@njcu.edu)[: msayed@njcu.edu](E-mail: msayed@njcu.edu) |\n| --- |\n| | ABSTRACT\u003Cbr>Parkinson's disease (PD) is a prevalent neurodegenerative disorder known for its impact on motor neurons, causing symptoms like tremors, stiffness, and gait difficulties. This study explores the potential of vocal feature alterations in PD patients as a means of early disease prediction. This research aims to predict the onset of Parkinson's disease. Utilizing a variety of advanced machine-learning algorithms, including XGBoost, LightGBM, Bagging, AdaBoost, and Support Vector Machine, among others, the study evaluates the predictive performance of these models using metrics such as accuracy, area under the curve (AUC), sensitivity, and specificity. The findings of this comprehensive analysis highlight LightGBM as the most effective model, achieving an impressive accuracy rate of 96% alongside a matching AUC of 96% . LightGBM exhibited a remarkable sensitivity of 100% and specificity of 94.43%, surpassing other machine learning algorithms in accuracy and AUC scores. Given the complexities of Parkinson's disease and its challenges in early diagnosis, this study underscores the significance of leveraging vocal biomarkers coupled with advanced machine-learning techniques for precise and timely PD detection.\u003Cbr>| KEYWORDS\u003Cbr>RGB-D camera, Microsoft Kinect, Parkinson's disease assessment, gait analysis, skeleton data, deep brain stimulation, gait parameters, discrimination, PD ON and PD OFF states, center shoulder velocity, cost-effective, portable system, hospital environment\u003Cbr>| ARTICLE INFORMATION\u003Cbr>ACCEPTED: 05 November 2023 PUBLISHED: 02 December 2023 DOI: 10. 32996/jcsts.2023.5.4.14 |\n\n1. Introduction  \nParkinson's disease (PD) is a significant neurodegenerative disorder that predominantly affects motor neurons, leading to distressing symptoms such as tremors, stiffness, and difficulties in controlling gait. PD stands out among neurodegenerative conditions due to its profound impact on motor functions and subsequent decline in quality of life. This study explores vocal feature alterations as potential indicators for early PD prediction in the pursuit of improved diagnosis and timely intervention. Leveraging the intricate connection between vocal characteristics and neurological disorders, particularly PD, offers a promising avenue for enhancing diagnostic accuracy and prognosis.  \nCopyright: © 2023 the Author(s) . This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) 4.0 license ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)) . 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