[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123964-en":3,"doc-seo-123964-105":30,"detail-sidebar-cat-0-en-105":92},{"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":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},123964,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",7,"Healthcare","A Machine Learning Approach to Detect Parkinson's Disease by Looking at Gait Alterations","Parkinson's disease (PD) is frequently diagnosed late, after substantial loss of nigrostriatal dopaminergic projections, creating an urgent need for early, reliable biomarkers—especially for individuals at higher risk. This study investigates machine learning–based PD diagnosis from gait alterations recorded with inertial sensors worn during walking through a 15 m corridor in three scenarios. Six models (SVM, logistic regression, neural networks, kNN, decision trees, random forest) are trained while addressing limited dataset size. The best model achieves over 80% accuracy with precision and specificity above 90% and sensitivity around 71% (41 PD patients, 36 controls).","mathematics  \nArticle  \nA Machine Learning Approach to Detect Parkinson's Disease by Looking at Gait Alterations  \nCristina Tîrn˘auc˘a 1, *, Diana Stan 1, Johannes Mario Meissner 2, Diana Salas-Gómez 3,  \nMario Fernández-Gorgojo 3 and Jon Infante 4,5,6  \nCitation: Tîrn˘auc˘a, C.; Stan, D.; Meissner, J.M.; Salas-Gómez, D.; Fernández-Gorgojo, M.; Infante, J. A Machine Learning Approach to Detect Parkinson's Disease by Looking at Gait Alterations. Mathematics 2022, 10, 3500. [https://](https://)[ ](https://)[doi.org/10.3390/math10193500](doi.org/10.3390/math10193500)  \nAcademic Editor: Radu Tudor Ionescu  \nReceived: 16 August 2022  \nAccepted: 21 September 2022  \nPublished: 25 September 2022  \nPublisher's Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.  \nCopyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Departamento de Matemáticas, Estadística y Computación, Universidad de Cantabria, 39005 Santander, Spain  \n2 Computer Science Department, Graduate School of Information Science and Technology, The University of Tokyo, Tokyo 113-8656, Japan  \n3 Movement Analysis Laboratory, Physiotherapy School Cantabria, Escuelas Universitarias Gimbernat (EUG), Universidad de Cantabria, 39300 Torrelavega, Spain  \n4 Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED),  \n28029 Madrid, Spain  \n5 Neurology Service, University Hospital Marqués de Valdecilla—IDIVAL, 39008 Santander, Spain  \n6 Departamento de Medicina y Psiquiatría, Universidad de Cantabria, 39011 Santander, Spain  \n* Correspondence: cristina.tirnauca@unican.es; Tel.: +34-942-203-941  \nAbstract: Parkinson's disease (PD) is often detected only in later stages, when about 50% of nigrostriatal dopaminergic projections have already been lost. Thus, there is a need for biomarkers to monitor the earliest phases, especially for those that are at higher risk. In this work, we explore the use of machine learning methods to diagnose PD by analyzing gait alterations via an inertial sensors system that participants in the study wear while walking down a 15 m long corridor in three different scenarios. To achieve this goal, we have trained six well-known machine learning models: support vector machines, logistic regression, neural networks, k nearest neighbors, decision trees and random forest. We thoroughly explored several ways to mitigate the problems derived from the small amount of available data. We found that, while achieving accuracy rates of over 70% is quite common, the accuracy of the best model trained is only slightly above the 80% mark. This model has high precision and specificity (over 90%), but lower sensitivity (only 71%) . We believe that these results are promising, especially given the size of the population sample (41 PD patients and 36 healthy controls), and that this research venue should be further explored.  \nKeywords: Parkinson's disease; gait alterations; classification; support vector machine; logistic regression; neural networks; k nearest neighbors; decision trees; random forest  \nMSC: 68T05  \n1. Introduction  \nParkinson's disease (PD) is a disorder of the central nervous system that progressively alter the body's motor capacities. Symptoms present insidiously, in the form of tremor or clumsiness or slowness of movement. In early stages of the disease, the patient's facial expression may not show any signs. While the disease progresses, speech maybe altered, as well as the movement of arms while walking. Other serious problems such as dementia and difficulties thinking, eating or sleeping or issues such as depression may appear, although in more advanced stages. PD cannot","cbCaibDygzKuelCo","https://ap.wps.com/l/cbCaibDygzKuelCo","pdf",4303901,1,25,"English","en",105,"# Introduction\n## Background on Parkinson's disease and early diagnosis\n## Motivation for gait-based biomarkers\n# Methods\n## Data collection with inertial sensors\n## Machine learning models and training strategy\n# Results\n## Accuracy, precision, specificity, and sensitivity\n## Model comparison and impact of limited data\n# Discussion and Conclusion\n## Interpretation of findings and future directions","[{\"question\":\"Why is early detection of Parkinson's disease important in this work?\",\"answer\":\"Many patients are diagnosed only after symptoms are visible and substantial dopaminergic loss has occurred. The study targets earlier phases by seeking biomarkers detectable through gait alterations.\"},{\"question\":\"How is the gait data collected for machine learning in the study?\",\"answer\":\"Participants wear inertial sensors while walking down a 15 m corridor under three different scenarios, generating gait measurements used for classification.\"},{\"question\":\"Which machine learning models are trained and compared?\",\"answer\":\"The work trains six common models: support vector machines, logistic regression, neural networks, k nearest neighbors, decision trees, and random forest, then evaluates and compares their performance.\"}]","A Machine Learning Approach to Detect Parkinson's Disease by Looking at Gait Alterations | PDF",1785819469,63,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"a-machine-learning-approach-to-detect-parkinsons-disease-by-looking-at-gait-alterations","",{"@graph":36,"@context":86},[37,54,69],{"@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/a-machine-learning-approach-to-detect-parkinsons-disease-by-looking-at-gait-alterations/123964/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-04",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is early detection of Parkinson's disease important in this work?","Question",{"text":76,"@type":77},"Many patients are diagnosed only after symptoms are visible and substantial dopaminergic loss has occurred. The study targets earlier phases by seeking biomarkers detectable through gait alterations.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How is the gait data collected for machine learning in the study?",{"text":81,"@type":77},"Participants wear inertial sensors while walking down a 15 m corridor under three different scenarios, generating gait measurements used for classification.",{"name":83,"@type":74,"acceptedAnswer":84},"Which machine learning models are trained and compared?",{"text":85,"@type":77},"The work trains six common models: support vector machines, logistic regression, neural networks, k nearest neighbors, decision trees, and random forest, then evaluates and compares their performance.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,119,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":117,"slug":118},40,"healthcare",{"id":120,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":122,"slug":123},8,"Research & Report",30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]