[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124198-en":3,"doc-seo-124198-105":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},124198,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","A quantum inspired machine learning approach for multimodal Parkinsons disease screening","Parkinson’s disease is a rapidly increasing neurodegenerative disorder whose progression degrades speech, memory, and motor control, making early diagnosis essential for preserving quality of life. While machine-learning detection is promising, many studies rely on single-feature inputs and can fail under symptom variability. Using the mPower dataset with four biomarkers and extracted 64 features, the work trains feature selection with a Random Forest baseline and builds a simulatable quantum SVM to capture high-dimensional patterns. Results reach 90% accuracy, F1=0.90, and AUC=0.98, outperforming benchmarks and supporting accessible screening.","UC Davis  \nUC Davis Previously Published Works  \nTitle  \nA quantum inspired machine learning approach for multimodal Parkinsons disease screening.  \nPermalink  \n[https://escholarship.org/uc/item/2dc4j50v](https://escholarship.org/uc/item/2dc4j50v)  \nJournal  \nScientific Reports, 15(1)  \nISSN  \n2045-2322  \nAuthors  \nVatsavai, Diya  \nIyer, Anya Nair, Ashwin  \nPublication Date  \n2025-04-01  \nDOI  \n10.1038/s41598-025-95315-0  \nCopyright Information  \nThis work is made available under the terms of a Creative Commons Attribution License, available at [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \n[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nA quantum inspired machine learning approach for multimodal Parkinson’s disease screening  \nDiyaVatsavai1, Anya Iyer2 & AshwinA. Nair3􀀍  \nParkinson’s disease, currently the fastest-growing neurodegenerative disorder globally, has seen a 50% increase in cases within just two years. As disease progression impairs speech, memory, and motor functions over time, early diagnosis is crucial for preserving patients’ quality of life. Although machine-learning-based detection has shown promise for detecting Parkinson’s disease, most studies rely on a single feature for classification and can be error-prone due to the variability of symptoms between patients. To address this limitation we utilized the mPower dataset, which includes 150,000 samples across four key biomarkers: voice, gait, tapping, and demographic data. From these measurements, we extracted 64 features and trained a baseline Random Forest model to select the features above the 80th percentile. For classification, we designed a simulatable quantum support vector machine (qSVM) that detects high-dimensional patterns, leveraging recent advancements in quantum machine learning. With this novel and simulatable architecture that can be run on standard hardware rather than resource-intensive quantum computers, our model achieves an accuracy of 90%, F-1 score of 0.90, and an AUC of 0.98—surpassing benchmark models. Utilizing an innovative classification framework built on a diverse set of features, our model offers a pathway for accessible global Parkinson’s screening.  \nParkinson’s disease (PD) is a progressive neurodegenerative disorder that gradually compromises neuronal function, resulting in motor and nonmotor impairments1. Central to PD pathology is the degeneration of dopaminergic neurons within the substantia nigra, a critical region for dopamine synthesis. An essential neurotransmitter deficiency arises as these neurons lose their dopamine-producing capacity, leading to hallmark symptoms such as bradykinesia, hypokinetic dysarthria, resting tremor, and muscular rigidity2. Over the past 25 years, PD prevalence has doubled, and related deaths have increased by more than 100% since 20003. These trends highlight an urgent need for effective disease detection and intervention.  \nCurrent detection methods, like DAT and PET scans, are not PD-specific, often leading to imprecise results4. These diagnostic approaches also require expensive, specialized equipment and medical expertise, costing the U.S. around $14 billion annually5. As the fastest-growing neurological condition globally, PD urgently calls for a low-cost, high-accuracy screening solution6.  \nDespite recent improvements in clinical diagnostic criteria, accurately identifying PD remains difficult due to the overlap of symptoms with other neurodegenerative conditions and the normal aging process. Diagnoses based on a single or limited set of clinical features often lead to inconclusive results. Research on specific biomarkers has helped improve PD detection. In this study, we focus on vocal biomarkers, gait indicators, demographic data, and tapping metrics. Studies show that voice serve","cbCaisO39yrhJEVb","https://ap.wps.com/l/cbCaisO39yrhJEVb","pdf",2299637,1,14,"English","en",105,"# Introduction\n## Disease burden and need for early detection\n## Limitations of current diagnostic methods\n## Biomarkers: voice, gait, and demographic/tapping data","[{\"question\":\"Why is early screening for Parkinson’s disease important?\",\"answer\":\"Progressive neuronal impairment worsens speech, memory, and motor function, so early detection helps preserve patients’ quality of life.\"},{\"question\":\"What dataset and biomarkers are used in the study?\",\"answer\":\"The mPower dataset is used, containing 150,000 samples across voice, gait, tapping, and demographic data.\"},{\"question\":\"How does the proposed method perform classification?\",\"answer\":\"It selects features using a Random Forest baseline and then trains a simulatable quantum support vector machine (qSVM) to detect high-dimensional patterns.\"}]","A quantum inspired machine learning approach for multimodal Parkinsons disease screening | PDF",1785820980,35,{"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},"a-quantum-inspired-machine-learning-approach-for-multimodal-parkinsons-disease-screening","",{"@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/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/a-quantum-inspired-machine-learning-approach-for-multimodal-parkinsons-disease-screening/124198/",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},"Why is early screening for Parkinson’s disease important?","Question",{"text":75,"@type":76},"Progressive neuronal impairment worsens speech, memory, and motor function, so early detection helps preserve patients’ quality of life.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What dataset and biomarkers are used in the study?",{"text":80,"@type":76},"The mPower dataset is used, containing 150,000 samples across voice, gait, tapping, and demographic data.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the proposed method perform classification?",{"text":84,"@type":76},"It selects features using a Random Forest baseline and then trains a simulatable quantum support vector machine (qSVM) to detect high-dimensional patterns.","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},[93,97,101,105,110,115,120,123,128,131,135],{"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":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},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"]