[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-290555-105":59,"doc-detail-290555-en":130},{"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":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","predicting-parkinsons-disease-using-machine-learning-model","Predicting Parkinson’s Disease Using Machine Learning Model","","Research focuses on building a machine learning program for early detection of Parkinson’s disease (PD) using clinical and behavioral data, including motor and non-motor side effects. The study extracts highlights from persistent data and applies supervised learning to identify patterns that indicate PD. Multiple model families are evaluated, including support vector machines and neural systems, to determine the most effective approach for accurate forecasting, aiming to improve early diagnosis and support personalized treatment strategies.",{"@graph":69,"@context":122},[70,84,105],{"@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":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/predicting-parkinsons-disease-using-machine-learning-model/290555/",{"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/predicting-parkinsons-disease-using-machine-learning-model/290555.png","ImageObject",300,407,{"name":92,"@type":93},"Liam","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-22","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":52},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What data sources are used to predict Parkinson’s disease in the study?","Question",{"text":112,"@type":113},"The research uses clinical and behavioral data, including motor symptoms and non-motor side effects. It also discusses using features derived from persistent records to support supervised learning.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which machine learning methods are evaluated for disease prediction?",{"text":117,"@type":113},"The study assesses performance of multiple approaches, including back vector machines and neural systems, to find the most effective method for accurate predictions.",{"name":119,"@type":110,"acceptedAnswer":120},"Why is early and accurate diagnosis emphasized for Parkinson’s disease?",{"text":121,"@type":113},"The text highlights that PD is a progressive neurodegenerative disorder and that early, precise diagnosis is essential for effective management and treatment. It also notes that traditional subjective clinical evaluations can delay diagnosis.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},290555,1790101304,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":52,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":34,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":144},8796095461564,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","Predicting Parkinson’s Disease Using Machine Learning Model  \nSanjay Aswath K. S.M.1, Chitra K.2*  \n1, 2 Dayananda Sagar Academy of Technology and Management, Karnataka, India  \n*Email: [chitra-mca@dsatm.edu.in](chitra-mca@dsatm.edu.in)  \nAbstract  \nThis research work discusses the steps involved in developing a machine learning program for the early detection of Parkinson's disease (PD) using a variety of clinical and behavioral data. By utilizing highlights extracted from persistent data, including engine and non-motor side effects, the demonstration employs administered learning procedures to identify patterns indicative of Parkinson's disease (PD) . We assess the performance of various calculations, including back vector machines and neural systems, to determine the most effective method for accurate forecasts. The results demonstrate the model's potential to enhance early diagnosis and personalized treatment strategies for Parkinson's infection. Parkinson's disease (PD) is a dynamic neurodegenerative disorder characterized by engine side effects such as tremors, inflexibility, and bradykinesia, as well as non-motor side effects including cognitive disability and autonomic brokenness. Early and precise diagnosis is essential for effective management and treatment of the infection. In later years, machine learning (ML) has risen as an effective device in the field of therapeutic diagnostics, advertising potential changes in the early location and observation of Parkinson's malady.  \nKeywords  \nParkinson's Disease, Machine Learning, Early Prediction, Supervised Learning, Clinical Data  \nIntroduction  \nParkinson's malady (PD) is a persistent and dynamic neurodegenerative clutter that influences millions of individuals around the world. It is fundamentally characterized by engine indications such as tremors, bradykinesia, inflexibility, and postural flimsiness, but it moreover envelops a extend of non-motor side effects, counting cognitive disability, temperament disarranges, and autonomic brokenness. The complexity and heterogeneity of PD make early diagnosis and accurate prediction of disease progression particularly challenging. Traditional diagnostic methods rely heavily on clinical evaluations and the subjective interpretation of symptoms, which can lead to delayed diagnosis and suboptimal treatment outcomes.  \nMachine learning approaches leverage vast amounts of data from various sources, including clinical records, neuroimaging, genetic information, and wearable sensors. By analyzing these assorted datasets, ML models can recognize complex designs and biomarkers Submission: 1 6 October 2024; Acceptance: 15 November 2024  \nCopyright: © 2024. All the authors listed in this paper. The distribution, reproduction, and any other usage of the content of this paper is permitted, with credit given to all the author(s) and copyright owner(s) in accordance to common academic practice. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, as stated in the website: [https://creativecommons.org/licenses/](https://creativecommons.org/licenses/by/4.0/)[by/4.0/](https://creativecommons.org/licenses/by/4.0/)  \nrelated with the onset and movement of Parkinson's malady, which are regularly imperceptible by ordinary strategies. These models can help in recognizing PD from other neurodegenerative disarranges, anticipating malady direction, and fitting personalized treatment plans based on person understanding profiles.  \nParkinson's infection is a complex and progressive neurodegenerative disorder that significantly impacts millions of individuals worldwide, often resulting in delayed diagnosis and suboptimal treatment due to the reliance on subjective clinical evaluations. Traditional diagnostic methods fall short in leveraging the wealth of available data from neuroimaging, genetic profiles, and wearable sensors, necessitating the advancement of objective, rel","cbCaipHmkajBdRTW","https://ap.wps.com/l/cbCaipHmkajBdRTW","pdf",184838,"English","# Abstract\n# Keywords\n# Introduction\n# Literature review","[{\"question\":\"What data sources are used to predict Parkinson’s disease in the study?\",\"answer\":\"The research uses clinical and behavioral data, including motor symptoms and non-motor side effects. It also discusses using features derived from persistent records to support supervised learning.\"},{\"question\":\"Which machine learning methods are evaluated for disease prediction?\",\"answer\":\"The study assesses performance of multiple approaches, including back vector machines and neural systems, to find the most effective method for accurate predictions.\"},{\"question\":\"Why is early and accurate diagnosis emphasized for Parkinson’s disease?\",\"answer\":\"The text highlights that PD is a progressive neurodegenerative disorder and that early, precise diagnosis is essential for effective management and treatment. It also notes that traditional subjective clinical evaluations can delay diagnosis.\"}]","Predicting Parkinson’s Disease Using Machine Learning Model | PDF",1789642065,18]