[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120736-en":3,"doc-seo-120736-105":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":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},120736,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",7,"Healthcare","SYMPTOM ANALYSIS OF PARKINSON’S DISEASE UTILIZING MACHINE LEARNING METHODS - Bachelor’s thesis","Parkinson’s disease progression is typically monitored through symptom diaries and periodic neurologist visits, but diary entries based on patients’ memories are often unreliable and clinic appointments are burdensome. This thesis focuses on automating symptom evaluation using machine learning classifiers trained to recognize a patient’s current severity stage and to predict future outcomes such as progression rate or freezing of gait. Training data are collected with wearable sensors that capture gait parameters from multiple walking tasks and daily activities. The work summarizes studies from 2020–2023 and evaluates models via accuracy, sensitivity, and specificity, discussing gait analysis foundations and clinical criteria.","Pinja Koivisto  \nSYMPTOM ANALYSIS OF PARKINSON’S DISEASE UTILIZING MACHINE LEARNING METHODS  \nBachelor’s thesis  \nFaculty of Medicine and Health Technology Milla Juutinen  \nMay 2023  \nABSTRACT  \nPinja Koivisto: Symptom analysis of Parkinson’s disease utilizing machine learning methods Bachelor’s thesis  \nTampere University  \nBachelor's Program in Biotechnology and Biomedical Engineering  \nMay 2023  \nWhile monitoring Parkinson’s disease progression or observing the everchanging severity stage of the disease, the patients are keeping symptom diaries and making regular visits to the neurologist clinic for evaluation. The diaries are based on patients own memories which tend tobe unreliable in addition to the burdensome clinical appointments. Therefore, the research is focused on automatizing the burden with the help of machine learning classifiers. These classifiers are trained to either recognize the current severity stage of a patient or make a prediction about future outcomes, such as the progression rate of the disease or a freezing of gait event. The data on which the classifiers are trained with is gathered via wearable sensors that attain several gait parametrics from different walking tasks or daily activities conducted.  \nThis thesis presents several studies conducted during the years of 2020–2023 which aim to develop a machine learning algorithm to classify the correct state of the patient according to the disease stage , or predict medical outcomes before their occurring. Their performance metrics are evaluated , especially regarding their accuracy, sensitivity and specificity results. Additionally, this thesis introduces background of gait analysis and machine learning methods. The changes in gait that Parkinson’s disease inflicts are discussed alongside the clinical criteria used in evaluating the changes and patient’s condition.  \nThis thesis is a literature review, which aims to find the best possible machine learning algorithms for symptom analysis of Parkinson’s disease. It concludes that comprehensive conclusions are difficult to draw, since the algorithm performance can be analysed with several different metrics. Even though most of the algorithms gained adequate results, the research still includes several limitations to solve before the algorithm can be validated for clinical use as a symptom monitoring system.  \nKeywords: Parkinson’s disease, machine learning, motor symptoms, gait analysis, wearable sensors, automatization, symptom evaluation, performance metrics  \nThe originality of this thesis has been checked using the Turnitin OriginalityCheck service.  \nTIIVISTELMÄ  \nPinja Koivisto: Parkinsonin taudin oireiden arviointi koneoppimismenetelmien avulla Kandidaatintutkielma  \nTampereen yliopisto  \nBioteknologian ja biolääketieteen tekniikan kandidaattiohjelma  \nToukokuu 2023  \nParkinsonin taudin etenemisen seuranta perustuu potilaiden omiin oirepäiväkirjamerkintöihin. Lisäksi taudin jatkuvasti muuttuvaa vakavuusastetta seurataan säännöllisesti neurologin klinikalla. Päiväkirjat perustuvat potilaan omiin muistikuviin, jotka ovat yleensä epäluotettavia ja klinikalla käynti raskasta. Siksi tutkimus keskittyy taakan automatisointiin koneoppimismenetelmien avulla. Nämä algoritmit koulutetaan joko tunnistamaan taudin nykyinen vakavuusaste tai ennustamaan tulevia tuloksia, kuten taudin etenemisnopeutta tai kävelykyvyn jäätymistä . Tietoja, joillakoneoppimisalgoritmeja koulutetaan, kerätään puettavien sensoreiden avulla. Nämä keräävät dataa useista eri kävelyparametreista, jotka saadaan talteen erilaisia kävelytestejä hyödyntäen.  \nTässä työssä esitellään useita vuosina 2020–2023 tehtyjä tutkimuksia, joiden tarkoituksena on kehittää koneoppimisalgoritmeja, jotka luokittelevat potilaan oikeaan vakavuusastekategoriaan tai ennustavat lääketieteellisiä tuloksia ennen niiden ilmenemistä . Algoritmien suorituskykymittareita arvioidaan erityisesti tarkkuuden, herkkyyden ja spesifisyyden suhteen. Lisäksi työssä taustoiteta","cbCaih9Og1hr5MSM","https://ap.wps.com/l/cbCaih9Og1hr5MSM","pdf",720015,1,44,"English","en",105,"# Introduction\n# Gait measurements\n## Accelerometers and gyroscopes\n## Inertial measurement units\n## Change in the gait patterns of people with Parkinson’s disease\n## Measurement methods to extract gait parameters\n# Machine learning methods\n## Machine learning classifiers\n## Cross-validation\n## Classifier performance","[{\"question\":\"How does the thesis propose to monitor Parkinson’s disease symptoms?\",\"answer\":\"It uses machine learning classifiers to automate symptom evaluation by recognizing current severity stage and predicting future outcomes based on wearable-sensor data.\"},{\"question\":\"What data source is used to train and test the classifiers?\",\"answer\":\"Wearable sensors collect gait parameters during different walking tasks and daily activities.\"},{\"question\":\"How is classifier performance assessed in the thesis?\",\"answer\":\"Performance metrics are evaluated particularly using accuracy, sensitivity, and specificity.\"}]","SYMPTOM ANALYSIS OF PARKINSON’S DISEASE UTILIZING MACHINE LEARNING METHODS - Bachelor’s thesis | PDF",1785731772,111,{"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},"symptom-analysis-of-parkinsons-disease-utilizing-machine-learning-methods-bachelors-thesis","",{"@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/symptom-analysis-of-parkinsons-disease-utilizing-machine-learning-methods-bachelors-thesis/120736/",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","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How does the thesis propose to monitor Parkinson’s disease symptoms?","Question",{"text":75,"@type":76},"It uses machine learning classifiers to automate symptom evaluation by recognizing current severity stage and predicting future outcomes based on wearable-sensor data.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data source is used to train and test the classifiers?",{"text":80,"@type":76},"Wearable sensors collect gait parameters during different walking tasks and daily activities.",{"name":82,"@type":73,"acceptedAnswer":83},"How is classifier performance assessed in the thesis?",{"text":84,"@type":76},"Performance metrics are evaluated particularly using accuracy, sensitivity, and specificity.","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},[93,97,101,105,110,115,118,123,128,131,135],{"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":116,"slug":117},40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]