[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127808-en":3,"doc-seo-127808-105":31,"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127808,1099523885074,"Ivy","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Application of Machine Learning in the Diagnosis of Parkinson’s Disease - October 2023","Parkinson’s Disease (PD) is a progressive neurodegenerative disorder with both motor impairment and non-motor symptoms, including cognitive impairment, creating major obstacles for reliable diagnosis and accurate symptom assessment. Current “gold standard” clinical evaluation depends on visual judgement and therefore introduces subjectivity. This thesis applies objective machine-learning methods to two movement-data types to model motor severity and support more granular assessment of cognitive impairment in people with PD.","Application of Machine Learning in the Diagnosis of Parkinson’s  \nDisease  \nCameron Harwood  \nPhD  \nUniversity of York Physics, Engineering and Technology  \nOctober 2023  \n2  \nAbstract  \nParkinson’s Disease (PD) is a progressive neurodegenerative disorder characterised by both motor impairment and non-motor symptoms, including cognitive impairment. PD presents significant challenges for reliable diagnosis and accurate symptom assessment. The current“gold standard” clinical assessments rely on visual judgement, introducing subjectivity. This thesis aims to mitigate these limitations by applying objective machine learning methodologies to two distinct types of movement data, simple hand motor tasks and neuropsychological graphmotor assessments, with the objective of modeling motor severity and a potential for amore granular approach for assessing cognitive impairment for people with PD.  \nFor the hand motor tasks, end-to-end time series classification models were used to analyse positional data collected from 47 healthy controls and 148 PD patients. These models were applied for the diagnosis of PD and for the detection of clinically slight bradykinesia. After employing a 5-fold nested cross-validation strategy, the top-performing models achieved an accuracy rate of 84% for PD diagnosis and 82% for bradykinesia detection. These models provide an agile, objective, and rapid framework for hand kinematic assessments, negating the need for domain-specific knowledge. They have the potential to serve as essential tools for preliminary research in the field of kinematic evaluations.  \nFor the drawing assessments, the structural components of the Benson Complex Figure were identified with a top accuracy rate of 96%, following the novel investigation of encoding pen-dynamics. This enables the extraction of cognitive features related to the organisational strategy employed by the subjects.  \nCollectively, these findings introduce promising new data-driven approaches for the modeling of PD diagnosis and cognitive states. Importantly, the research is designed with the aim of integrating these methodologies into routine clinical practice and aligning with current research interests, thus laying the groundwork for future domain-specific studies in PD assessment.  \n4  \nContents  \nAbstract 3  \nContents 5  \nList of Figures 12  \nList of Tables 16  \nAcknowledgements 18  \nDeclaration 20  \n1 Introduction 22  \n1.1 Clinical Motivation ............................... 22  \n1.2 Research Interest and Objectives ...................... 24  \n1.3 Structure of Thesis ............................... 26  \n2 Parkinson’s Disease: Clinical Presentation and Assessment 28  \n2.1 Clinical Presentation of Parkinson’s Disease ............... 28  \n2.2 Clinical Assessments .............................. 33  \n2.2.1 Motor .................................... 33  \n2.2.2 Cognitive .................................. 35  \n2.3 Current Application of Digital Sensors ................... 35  \n2.4 Summary ..................................... 36  \n3 Supervised Machine Learning 39  \n3.1 Introduction ................................... 39  \n3.2 Evaluation of Classifiers ............................ 41  \n3.2.1 The Confusion Matrix, its Measures and Derived Metrics ....... 41  \n3.2.1.1 Accuracy ............................. 42  \n3.2.1.2 Precision, Sensitivity (Recall) and Specificity ......... 42  \n3.2.1.3 F1-Score ............................. 43  \n3.2.1.4 Matthew’s Correlation Coefficient ............... 43  \n3.2.1.5 The Receiver Operating Characteristic (ROC) Area Under the Curve (AUC) ........................ 43  \n3.2.2 Metrics for the Semantic Segmentation of Images ............ 44  \n3.2.2.1 Jaccard Similarity Coefficient .................. 45  \n3.2.3 Generalisability ............................... 45  \n3.3 Traditional Machine Learning Techniques ................. 48  \n3.3.1 Linear Regression .............................. 48  \n3.3.2 Logistic Regression ............................","cbCaiuHsWkcgITjI","https://ap.wps.com/l/cbCaiuHsWkcgITjI","pdf",33636716,3,1,200,"English","en",105,"# Abstract\n# Contents\n## Introduction\n## Parkinson’s Disease: Clinical Presentation and Assessment\n## Supervised Machine Learning\n## Diagnosing Parkinson’s Disease and Clinically Slight Bradykinesia From Raw Positional Data","[{\"question\":\"What problem does the thesis address in Parkinson’s disease diagnosis?\",\"answer\":\"It targets the subjectivity of current clinical “gold standard” assessments and the need for more reliable, objective evaluation of motor severity and cognitive impairment.\"},{\"question\":\"How are hand motor task data used for machine learning?\",\"answer\":\"End-to-end time-series classification models analyze positional data from healthy controls and PD patients to diagnose PD and detect clinically slight bradykinesia using nested cross-validation.\"},{\"question\":\"How are drawing assessments used to assess cognitive states?\",\"answer\":\"The thesis identifies structural components of the Benson Complex Figure and uses novel encoding of pen-dynamics to extract cognitive features related to subjects’ organisational strategy.\"}]","Application of Machine Learning in the Diagnosis of Parkinson’s Disease - October 2023 | PDF",1785941932,504,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"application-of-machine-learning-in-the-diagnosis-of-parkinsons-disease-october-2023","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/document/research-report/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/application-of-machine-learning-in-the-diagnosis-of-parkinsons-disease-october-2023/127808/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",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},"What problem does the thesis address in Parkinson’s disease diagnosis?","Question",{"text":76,"@type":77},"It targets the subjectivity of current clinical “gold standard” assessments and the need for more reliable, objective evaluation of motor severity and cognitive impairment.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How are hand motor task data used for machine learning?",{"text":81,"@type":77},"End-to-end time-series classification models analyze positional data from healthy controls and PD patients to diagnose PD and detect clinically slight bradykinesia using nested cross-validation.",{"name":83,"@type":74,"acceptedAnswer":84},"How are drawing assessments used to assess cognitive states?",{"text":85,"@type":77},"The thesis identifies structural components of the Benson Complex Figure and uses novel encoding of pen-dynamics to extract cognitive features related to subjects’ organisational strategy.","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":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]