[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127602-en":3,"doc-seo-127602-105":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},127602,549768064778,"Finn","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Diagnostic classification of Parkinson’s disease based on non-motor manifestations and machine learning strategies","Non-motor manifestations of Parkinson’s disease (PD) often appear early and strongly influence patients’ quality of life, yet their predictive value using machine learning remains insufficiently studied. This work evaluates nine algorithms to distinguish PD patients from controls using non-motor clinical PD features from Biocruces (96 subjects) and PPMI (687 subjects), and tests whether combining datasets improves performance through two granularity versions plus feature selection. Support Vector Machine and Multi-Layer Perceptron achieve the best accuracy (86.3% and 84.7%). Feature selection reduces variables and improves results. Cross-dataset enrichment moderately benefits recall (with slight precision decline). RIPPER provides interpretable rules using two variables—autonomic manifestations and olfactory dysfunction—achieving 84.4% accuracy. The study supports ML-based screening using the most discriminative non-motor parameters.","Neural Computing and Applications (2023) 35:5603–5617  \n[https://doi.org/10.1007/s00521-022-07256-8](https://doi.org/10.1007/s00521-022-07256-8)  \nS . I. : COMPUTATIONAL-BASED BIOMARKERS FOR MENTAL AND EMOTIONAL HEALTH(CBMEH2021)  \nDiagnostic classification of Parkinson’s disease based on non-motor manifestations and machine learning strategies  \nMaitane Martinez-Eguiluz 1  • Olatz Arbelaitz1  • Ibai Gurrutxaga1  • Javier Muguerza1  •  \nIigo Perona1  • Ane Murueta-Goyena2,3  • Marian Acera3  • Roco Del Pino3  • Beatriz Tijero3,4  • Juan Carlos Gomez-Esteban3,4,5  • Iigo Gabilondo3,4,5   \nReceived: 15 November 2021/Accepted: 29 March 2022/Published online: 6 May 2022  \n􀀂 The Author(s) 2022  \nAbstract  \nNon-motor manifestations of Parkinson’s disease (PD) appear early and have a significant impact on the quality of life of patients, but few studies have evaluated their predictive potential with machine learning algorithms. We evaluated 9 algorithms for discriminating PD patients from controls using a wide collection of non-motor clinical PD features from two databases: Biocruces (96 subjects) and PPMI (687 subjects) . In addition, we evaluated whether the combination of both databases could improve the individual results. For each database 2 versions with different granularity were created and a feature selection process was performed. We observed that most of the algorithms were able to detect PD patients with high accuracy ([80%) . Support Vector Machine and Multi-Layer Perceptron obtained the best performance, with an accuracy of 86.3% and 84.7%, respectively. Likewise, feature selection led to a significant reduction in the number of variables and to better performance. Besides, the enrichment of Biocruces database with data from PPMI moderately benefited the performance of the classification algorithms, especially the recall and to a lesser extent the accuracy, while the precision worsened slightly. The use of interpretable rules obtained by the RIPPER algorithm showed that simply using two variables (autonomic manifestations and olfactory dysfunction), it was possible to achieve an accuracy of 84.4% . Our study demonstrates that the analysis of non-motor parameters of PD through machine learning techniques can detect PD patients with high accuracy and recall, and allows us to select the most discriminative non-motor variables to create potential tools for PD screening.  \nKeywords Parkinson’s disease 􀀂 Machine Learning 􀀂 Early detection 􀀂 Non-motor symptoms  \n1 Introduction  \nParkinson’s disease (PD) is the second most common neurodegenerative condition after Alzheimer’s disease and affects up to 1% of the population above 60 years [1] . Cardinal motor symptoms such as bradykinesia, rigidity, and resting tremor are essential for PD diagnosis. These motor features emerge when approximately 50% of dopaminergic cells in the substantia nigra have degenerated [2, 3] and 70% of striatum dopaminergic synapses are lost [4] . Therefore, the clinical onset of PD is insidious, and by  \nExtended author information available on the last page of the article  \nthe time of diagnosis the development of brain pathology isin advanced stages.  \nEarly and accurate detection of PD is crucial for successful outcomes of disease-modifying therapies to slowdown—or even halt—disease progression. Towards this end, clinical features predating motor symptoms might be useful. It is increasingly recognized that non-motor manifestations, including olfactory dysfunction, autonomic symptoms, sleep disorders, visual impairment, cognitive decline or depressive symptoms, not only accompany but usually precede the onset of motor features [5, 6] . This premotor or prodromal phase in PD lasts between 5 to 20 years, and there is an increasing interest in using this array  \nof premotor manifestations to identify PD patients at very early stages.  \nIn the last few decades, machine learning techniques are being increasingly applied for the early diagnosis of PD. This has ","cbCairvMmFIGFben","https://ap.wps.com/l/cbCairvMmFIGFben","pdf",2377710,1,15,"English","en",105,"# Abstract\n# Keywords\n# 1 Introduction\n## Background: non-motor symptoms and early detection\n## Machine learning for early diagnosis of PD\n## Prior work using non-motor symptoms","[{\"question\":\"What problem does this study address?\",\"answer\":\"Non-motor manifestations of Parkinson’s disease appear early, but few studies have tested their predictive and diagnostic potential using machine learning.\"},{\"question\":\"Which datasets and machine learning models are used?\",\"answer\":\"The study uses non-motor clinical PD features from Biocruces and PPMI, evaluating nine algorithms for discriminating PD patients from controls.\"},{\"question\":\"What key performance results and variables are identified?\",\"answer\":\"Support Vector Machine and Multi-Layer Perceptron perform best with accuracies of 86.3% and 84.7%. With interpretable rules, RIPPER shows that autonomic manifestations and olfactory dysfunction alone can reach 84.4% accuracy.\"}]","Diagnostic classification of Parkinson’s disease based on non-motor manifestations and machine learning strategies | PDF",1785940221,38,{"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},"diagnostic-classification-of-parkinsons-disease-based-on-non-motor-manifestations-and-machine-learning-strategies","",{"@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/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/diagnostic-classification-of-parkinsons-disease-based-on-non-motor-manifestations-and-machine-learning-strategies/127602/",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","2026-08-22","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 this study address?","Question",{"text":76,"@type":77},"Non-motor manifestations of Parkinson’s disease appear early, but few studies have tested their predictive and diagnostic potential using machine learning.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which datasets and machine learning models are used?",{"text":81,"@type":77},"The study uses non-motor clinical PD features from Biocruces and PPMI, evaluating nine algorithms for discriminating PD patients from controls.",{"name":83,"@type":74,"acceptedAnswer":84},"What key performance results and variables are identified?",{"text":85,"@type":77},"Support Vector Machine and Multi-Layer Perceptron perform best with accuracies of 86.3% and 84.7%. With interpretable rules, RIPPER shows that autonomic manifestations and olfactory dysfunction alone can reach 84.4% accuracy.","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},[94,98,102,106,111,116,121,124,129,132,136],{"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":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},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"]