{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T05:18:04Z","timestamp":1784956684824,"version":"3.55.0"},"reference-count":39,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2025,8,12]],"date-time":"2025-08-12T00:00:00Z","timestamp":1754956800000},"content-version":"vor","delay-in-days":11,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Brain and Behavior"],"published-print":{"date-parts":[[2025,8]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study aims to create a reliable and scalable framework for detecting Parkinson's disease (PD) using spiral drawings. It integrates advanced machine learning techniques to improve diagnostic accuracy and practical application in clinical settings.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>Spiral drawing data were collected from a comprehensive dataset, including samples from both Parkinson's patients and healthy individuals. Three deep learning models\u2014ResNet50, VGG16, and EfficientNetB0\u2014were used to extract detailed patterns from the drawings. To enhance model performance, four feature selection techniques were applied: Principal Component Analysis (PCA), Recursive Feature Elimination (RFE), Least Absolute Shrinkage and Selection Operator (LASSO), and ANOVA. Six different classifiers (Support Vector Machine [SVM], Random Forest [RF], Multi\u2010Layer Perceptron [MLP], XGBoost, CatBoost, and voting classifiers) were tested. The system's diagnostic accuracy was measured using four metrics: accuracy, sensitivity, F1\u2010score, and AUC\u2010ROC. Heatmaps and ROC curves were created to visualize the results.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The models achieved high classification performance with different configurations. For example, ResNet50 with PCA and MLP reached the highest accuracy (98%) and AUC\u2010ROC (97%). Similarly, SVM with PCA achieved accuracy (92%) and AUC\u2010ROC (98%). For VGG16, combining LASSO with XGBoost resulted in high F1\u2010scores (90%) and AUC\u2010ROC (93%), while the voting classifiers with PCA achieved an AUC\u2010ROC of 98%. EfficientNetB0 combined with RFE and XGBoost delivered exceptional accuracy (98%) with robust overall metrics. CatBoost with LASSO achieved balanced performance, showing high sensitivity (89%) and AUC\u2010ROC (96%). Ensemble methods, like voting classifiers, consistently provided strong AUC\u2010ROC values but showed variability in accuracy and sensitivity compared to individual classifiers like MLP and SVM.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The study demonstrated that combining advanced techniques for feature extraction, selection, and classification can significantly improve PD detection accuracy. Future research should focus on integrating multiple data sources and exploring real\u2010time applications to enhance scalability and clinical utility.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/brb3.70770","type":"journal-article","created":{"date-parts":[[2025,8,12]],"date-time":"2025-08-12T11:01:23Z","timestamp":1754996483000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A Comprehensive Framework for Parkinson's Disease Detection Using Spiral Drawings and Advanced Machine Learning Techniques"],"prefix":"10.1002","volume":"15","author":[{"given":"Mohamed J.","family":"Saadh","sequence":"first","affiliation":[{"name":"Faculty of Pharmacy Middle East University  Amman Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Waleed K.","family":"Abdulsahib","sequence":"additional","affiliation":[{"name":"Department of Pharmacology and Toxicology, College of Pharmacy Al Farahidi University  Baghdad Iraq"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hardik","family":"Doshi","sequence":"additional","affiliation":[{"name":"Marwadi University Research Center, Department of Computer Engineering, Faculty of Engineering and Technology Marwadi University  Rajkot Gujarat India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anupam","family":"Yadav","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering and Applications GLA University  Mathura India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J.","family":"Gowrishankar","sequence":"additional","affiliation":[{"name":"Department of Computer Science Engineering, School of Engineering and Technology JAIN (Deemed to Be University)  Bengaluru Karnataka India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mayank","family":"Kundlas","sequence":"additional","affiliation":[{"name":"Centre For Research Impact and Outcome, Chitkara University Institute of Engineering and Technology Chitkara University  Rajpura Punjab India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nargiza","family":"Mansurova","sequence":"additional","affiliation":[{"name":"Medical Faculty Central Asian University  Tashkent Uzbekistan"},{"name":"Tashkent Pediatric Medical Institute  Tashkent Uzbekistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kamal Kant","family":"Joshi","sequence":"additional","affiliation":[{"name":"Department of Allied Science Graphic Era Hill University  Dehradun Uttarakhand India"},{"name":"Graphic Era Deemed to Be University  Dehradun Uttarakhand India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fadhil Feez","family":"Sead","sequence":"additional","affiliation":[{"name":"Department of Dentistry, College of Dentistry The Islamic University  Najaf Iraq"},{"name":"Department of Medical analysis, Medical Laboratory Technique College The Islamic University of Al Diwaniyah  Al Diwaniyah Iraq"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2290-7220","authenticated-orcid":false,"given":"Bagher","family":"Farhood","sequence":"additional","affiliation":[{"name":"Department of Medical Physics and Radiology, Faculty of Paramedical Sciences Kashan University of Medical Sciences  Kashan Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,8,12]]},"reference":[{"key":"e_1_2_14_2_1","doi-asserted-by":"crossref","unstructured":"Abdullah N. A. S. F. A.Hanan andA. H. A.Rahman.2024. \u201cParkinson's Disease Detection through Spiral Drawing Recognition Using Machine Learning Approach.\u201d InInternational Conference on Innovation & Entrepreneurship in Computing Engineering & Science Education (InvENT 2024) 341\u2013351.Atlantis Press.","DOI":"10.2991\/978-94-6463-589-8_31"},{"key":"e_1_2_14_3_1","doi-asserted-by":"crossref","unstructured":"Aversano L. M. L.Bernardi M.Cimitile M.Iammarino andC.Verdone.2022. \u201cEarly Detection of Parkinson's Disease Using Spiral Test and Echo State Networks.\u201d In2022 International Joint Conference on Neural Networks (IJCNN) 1\u20138.IEEE.","DOI":"10.1109\/IJCNN55064.2022.9891917"},{"key":"e_1_2_14_4_1","doi-asserted-by":"publisher","DOI":"10.3390\/bioengineering11070643"},{"key":"e_1_2_14_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(21)00218-X"},{"key":"e_1_2_14_6_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41531-022-00439-z"},{"key":"e_1_2_14_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0896-6273(03)00568-3"},{"key":"e_1_2_14_8_1","doi-asserted-by":"publisher","DOI":"10.1093\/bmb\/ldn013"},{"key":"e_1_2_14_9_1","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2024.1453743"},{"key":"e_1_2_14_10_1","doi-asserted-by":"publisher","DOI":"10.1002\/mds.22672"},{"key":"e_1_2_14_11_1","doi-asserted-by":"publisher","DOI":"10.1002\/mds.22017"},{"key":"e_1_2_14_12_1","doi-asserted-by":"crossref","unstructured":"Gil\u2010Mart\u00edn M. C.Luna\u2010Jim\u00e9nez F.Fern\u00e1ndez\u2010Mart\u00ednez andR.San\u2010Segundo.2023. \u201cSignal and Visual Approaches for Parkinson's Disease Detection From Spiral Drawings.\u201d InProceedings of the Northern Lights Deep Learning Workshop 1\u20138.Septentrio Academic Publishing.","DOI":"10.7557\/18.6809"},{"key":"e_1_2_14_13_1","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics13101691"},{"key":"e_1_2_14_14_1","doi-asserted-by":"publisher","DOI":"10.3390\/info15040220"},{"key":"e_1_2_14_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00702-017-1821-9"},{"key":"e_1_2_14_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.measen.2021.100047"},{"key":"e_1_2_14_17_1","doi-asserted-by":"crossref","unstructured":"Kasab M. N.Al Masri andR.Al Moghrabi.2024. \u201cDetection of Parkinson's Disease through Spiral Drawings Using Convolutional Autoencoders.\u201d In2024 International Conference on Smart Systems and Power Management (IC2SPM) 1\u20135.IEEE.","DOI":"10.1109\/IC2SPM62723.2024.10841337"},{"key":"e_1_2_14_18_1","doi-asserted-by":"crossref","unstructured":"Kibtia H. S.Abdullah andA.Bustamam.2020. \u201cComparison of Random Forest and Support Vector Machine for Prediction of Cognitive Impairment in Parkinson's Disease.\u201d InAIP Conference Proceedings 20093.AIP Publishing LLC.","DOI":"10.1063\/5.0030332"},{"key":"e_1_2_14_19_1","doi-asserted-by":"publisher","DOI":"10.1002\/mds.24973"},{"key":"e_1_2_14_20_1","doi-asserted-by":"publisher","DOI":"10.1186\/s43055-024-01287-y"},{"key":"e_1_2_14_21_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41531-022-00288-w"},{"key":"e_1_2_14_22_1","unstructured":"Medasani S.2023. \u201cParkinson's Disease Detection Using Spiral Images (Hand Drawings).\u201dMendeley Data V1.10.17632\/fd5wd6wmdj.1."},{"key":"e_1_2_14_23_1","doi-asserted-by":"publisher","DOI":"10.3390\/s150923727"},{"issue":"7","key":"e_1_2_14_24_1","first-page":"S176","article-title":"Improving Outcomes Through Early Diagnosis of Parkinson's Disease","volume":"18","author":"Pagan F. L.","year":"2012","journal-title":"American Journal of Managed Care"},{"issue":"4","key":"e_1_2_14_25_1","first-page":"94","article-title":"Early Diagnosis of Parkinson's Disease: Recommendations From Diagnostic Clinical Guidelines","volume":"16","author":"Pahwa R.","year":"2010","journal-title":"American Journal of Managed Care"},{"key":"e_1_2_14_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2009.191"},{"key":"e_1_2_14_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.parkreldis.2021.04.016"},{"key":"e_1_2_14_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1353-8020(09)70793-X"},{"key":"e_1_2_14_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2024.106220"},{"key":"e_1_2_14_30_1","doi-asserted-by":"publisher","DOI":"10.1212\/WNL.0000000000002350"},{"key":"e_1_2_14_31_1","first-page":"906","article-title":"Longitudinal Clustering Analysis and Prediction of Parkinson's Disease Progression","volume":"12","author":"Saberi A.","year":"2021","journal-title":"Society of Nuclear Medicine"},{"key":"e_1_2_14_32_1","doi-asserted-by":"publisher","DOI":"10.1117\/12.2612998"},{"key":"e_1_2_14_33_1","doi-asserted-by":"publisher","DOI":"10.1134\/S1054661822030385"},{"key":"e_1_2_14_34_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.parkreldis.2015.09.027"},{"issue":"6","key":"e_1_2_14_35_1","doi-asserted-by":"crossref","first-page":"991","DOI":"10.18280\/isi.270616","article-title":"Detection of Parkinson's Disease Using the Spiral Diagram and Convolutional Neural Network","volume":"27","author":"Singh M.","year":"2022","journal-title":"Ing\u00e9nierie Des Syst\u00e8mes D'information"},{"key":"e_1_2_14_36_1","doi-asserted-by":"crossref","unstructured":"Sun Q. Y.Liu T. S.Chua andB.Schiele.2019. \u201cMeta\u2010Transfer Learning for Few\u2010Shot Learning.\u201d InProceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition 403\u2013412.IEEE.","DOI":"10.1109\/CVPR.2019.00049"},{"key":"e_1_2_14_37_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1474-4422(21)00030-2"},{"key":"e_1_2_14_38_1","doi-asserted-by":"crossref","unstructured":"Wang L. H.Liu X.Chen andY.Li.2024. \u201cA Lightweight Parkinson's Disease Diagnostic Method Based on Hand\u2010Drawn Spiral Lines.\u201d InProceedings of the 2024 4th International Conference on Bioinformatics and Intelligent Computing 217\u2013220.ACM.","DOI":"10.1145\/3665689.3665726"},{"key":"e_1_2_14_39_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2022.09.400"},{"key":"e_1_2_14_40_1","doi-asserted-by":"crossref","unstructured":"Wrobel K. R.Doroz P.Porwik T.Orczyk A. B.Cavalcante andM.Grajzer.2022. \u201cFeatures of Hand\u2010Drawn Spirals for Recognition of Parkinson's Disease.\u201d InAsian Conference on Intelligent Information and Database Systems 458\u2013469.Springer.","DOI":"10.1007\/978-3-031-21967-2_37"}],"container-title":["Brain and Behavior"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/brb3.70770","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T06:15:55Z","timestamp":1758867355000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/brb3.70770"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8]]},"references-count":39,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2025,8]]}},"alternative-id":["10.1002\/brb3.70770"],"URL":"https:\/\/doi.org\/10.1002\/brb3.70770","archive":["Portico"],"relation":{"has-review":[{"id-type":"doi","id":"10.1002\/BRB3.70770\/v1\/review1","asserted-by":"object"},{"id-type":"doi","id":"10.1002\/BRB3.70770\/v1\/decision1","asserted-by":"object"},{"id-type":"doi","id":"10.1002\/BRB3.70770\/v2\/response1","asserted-by":"object"},{"id-type":"doi","id":"10.1002\/BRB3.70770\/v2\/decision1","asserted-by":"object"}]},"ISSN":["2162-3279","2162-3279"],"issn-type":[{"value":"2162-3279","type":"print"},{"value":"2162-3279","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8]]},"assertion":[{"value":"2024-11-28","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-07-27","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-08-12","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70770"}}