[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124369-en":3,"doc-seo-124369-105":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":4,"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},124369,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",7,"Healthcare","Evaluation of machine learning algorithms in the early detection of Parkinson's disease - a comparative study","Parkinson's is a neurodegenerative disease that affects mainly older adults, with damage to neurons and increased α-synuclein accumulation across brainstem regions, while its causes are still not fully clarified. The study addresses the need for effective early detection methods by comparing machine learning models for Parkinson's identification. Logistic regression, SVM, decision trees, extra trees, KNN, random forests, AdaBoost, and gradient boosting are trained and evaluated using the Oxford University dataset with 23 attributes and 195 patient voice records. Model performance is measured using accuracy, sensitivity, precision, and F1, showing KNN as the top predictor at 95% accuracy.","Indonesian Journal of Electrical Engineering and Computer Science  \nVol. 35, No. 1, July 2024, pp. 222~237  \nISSN: 2502-4752, DOI: 10. 11591/ijeecs.v35 . i1 .pp222-237 􀂈 222  \n\n| Evaluation of machine learning algorithms in the early detection of Parkinson's disease: a comparative study\u003Cbr>Joselyn Zapata-Paulini1, Michael Cabanillas-Carbonell2\u003Cbr>1Graduate School, Universidad Continental, Lima, Peru\u003Cbr>2Faculty of Engineering, Universidad Privada del Norte, Lima, Peru |  |\n| --- | --- |\n| Article Info\u003Cbr>Article history:\u003Cbr>Received Jan 17, 2024 Revised Feb 16, 2024 Accepted Mar 16, 2024\u003Cbr>Keywords:\u003Cbr>Algorithm Comparative Early detection Machine learning Parkinson\u003Cbr>Corresponding Author: | ABSTRACT\u003Cbr>Parkinson's is a neurodegenerative disease that generally affects people over 60 years of age. The disease destroys neurons and increases the accumulation of α-synuclein in many parts of the brain stem, although at present its causes remain unknown. It is therefore a priority to identify a method that can detect the disease, and this is where machine learning models become important. This study aims to perform a comparative analysis of machine learning models focused on the early detection of Parkinson's disease. Logistic regression (LR), support vector machines (SVM), decision trees (DT), extra trees classifiers (ETC), K-nearest neighbors (KNN), random forests (RF), adaptive boosting (AdaBoost) and gradient boosting (GB) algorithms are described and developed to identify the one that offers the best performance. In the training stage, we used the Oxford University dataset for Parkinson's disease detection, which has a total of 23 attributes and 195 records on patient voice recordings. The article is structured into six sections, such as introduction, related work, methodology, results, discussions, and conclusions. The metrics of accuracy, sensitivity, F1 count, and precision were used to measure the models' performance. The results position the KNN model as the best predictor with 95% accuracy, precision, sensitivity, and F1 score.\u003Cbr>This is an open access article under the CC BY-SA license.\u003Cbr> |\n| Joselyn Zapata-Paulini\u003Cbr>Graduate School, Universidad Continental\u003Cbr>Alfredo Mendiola 5210, Los Olivos 15311, Lima, Perú Email: [70994337@continental.edu.pe](70994337@continental.edu.pe) |  |\n\n1. INTRODUCTION  \nParkinson's disease is a disorder that impacted approximately 6 million people globally in 2016 [1] . Over the past two decades, a significant increase in the incidence of this disease has been observed, although the reasons behind this increase are not fully elucidated [2], [3] . This condition is characterized by the progressive loss of neurons of the substantia nigra pars compacta [4] and the accumulation of α-synuclein indifferent areas of the brainstem, but its origin remains an unsolved enigma for the scientific community [5], [6] . With approximately 3% to 5% of cases linked to genetic bases and 16% to 36% related to hereditary factors, Parkinson's is now classified as a heterogeneous disease affecting various regions of the nervous system [7], [8] .  \nIn this context, the prevalence of Parkinson's disease in developed countries is about 3% in the general population and about 1% in people over 60 years of age [9], [10] . People of different ethnic backgrounds maybe affected, although men are slightly more predisposed to the disease [11], [12] . The age of onset, previously estimated at 50.8 years, is now around 60 years, and the first symptoms may appear between 21 and 40 years of age, sometimes extending into the 50 years [13], [14] . Definitive diagnosis of Parkinson's disease is made by autopsy; however, clinical diagnosis is made by diagnostic certainty: clinically possible, clinically probable, and clinically definite Parkinson's disease [15] .  \nThe standardized incidence rate worldwide is 8 to 18 cases per 100,000 inhabitants [16] . In Spain, an annual incidence rate of 168 per 100,000 inhabitants was calc","cbCailFV3bzF8PKN","https://ap.wps.com/l/cbCailFV3bzF8PKN","pdf",640964,1,16,"English","en",105,"# Introduction\n## Machine learning for disease prediction\n# Methodology\n## Models and dataset\n# Results\n## Evaluation metrics\n# Discussions\n# Conclusions","[{\"question\":\"What is the main objective of the study?\",\"answer\":\"To perform a comparative analysis of machine learning models for the early detection of Parkinson's disease, identifying which model achieves the best performance.\"},{\"question\":\"Which dataset and input type are used in the experiments?\",\"answer\":\"The training uses the Oxford University Parkinson's detection dataset with 23 attributes and 195 records based on patient voice recordings.\"},{\"question\":\"How are the models evaluated?\",\"answer\":\"The study measures accuracy, sensitivity, precision, and F1 score to assess each algorithm’s predictive performance.\"}]","Evaluation of machine learning algorithms in the early detection of Parkinson's disease - a comparative study | PDF",1785821855,40,{"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},"evaluation-of-machine-learning-algorithms-in-the-early-detection-of-parkinsons-disease-a-comparative-study","",{"@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/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/evaluation-of-machine-learning-algorithms-in-the-early-detection-of-parkinsons-disease-a-comparative-study/124369/",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","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main objective of the study?","Question",{"text":75,"@type":76},"To perform a comparative analysis of machine learning models for the early detection of Parkinson's disease, identifying which model achieves the best performance.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which dataset and input type are used in the experiments?",{"text":80,"@type":76},"The training uses the Oxford University Parkinson's detection dataset with 23 attributes and 195 records based on patient voice recordings.",{"name":82,"@type":73,"acceptedAnswer":83},"How are the models evaluated?",{"text":84,"@type":76},"The study measures accuracy, sensitivity, precision, and F1 score to assess each algorithm’s predictive performance.","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},[93,97,101,105,110,115,117,122,127,130,134],{"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":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":116},"healthcare",{"id":118,"doc_module":4,"doc_module_name":46,"category_name":119,"show_sort_weight":120,"slug":121},8,"Research & Report",30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]