{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T08:11:50Z","timestamp":1783411910571,"version":"3.54.6"},"reference-count":45,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,9,13]],"date-time":"2023-09-13T00:00:00Z","timestamp":1694563200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Parkinson\u2019s disease (PD) is a neurological disorder affecting the nerve cells. PD gives rise to various neurological conditions, including gradual reduction in movement speed, tremors, limb stiffness, and alterations in walking patterns. Identifying Parkinson\u2019s disease in its initial phases is crucial to preserving the well-being of those afflicted. However, accurately identifying PD in its early phases is intricate due to the aging population. Therefore, in this paper, we harnessed machine learning-based ensemble methodologies and focused on the premotor stage of PD to create a precise and reliable early-stage PD detection model named PDD-ET. We compiled a tailored, extensive dataset encompassing patient mobility, medication habits, prior medical history, rigidity, gender, and age group. The PDD-ET model amalgamates the outcomes of various ML techniques, resulting in an impressive 97.52% accuracy in early-stage PD detection. Furthermore, the PDD-ET model effectively distinguishes between multiple stages of PD and accurately categorizes the severity levels of patients affected by PD. The evaluation findings demonstrate that the PDD-ET model outperforms the SVR, CNN, Stacked LSTM, LSTM, GRU, Alex Net, [Decision Tree, RF, and SVR], Deep Neural Network, HOG, Quantum ReLU Activator, Improved KNN, Adaptive Boosting, RF, and Deep Learning Model techniques by the approximate margins of 37%, 30%, 20%, 27%, 25%, 18%, 19%, 27%, 25%, 23%, 45%, 40%, 42%, and 16%, respectively.<\/jats:p>","DOI":"10.3390\/info14090502","type":"journal-article","created":{"date-parts":[[2023,9,13]],"date-time":"2023-09-13T05:31:28Z","timestamp":1694583088000},"page":"502","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["PDD-ET: Parkinson\u2019s Disease Detection Using ML Ensemble Techniques and Customized Big Dataset"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9787-3689","authenticated-orcid":false,"given":"Kalyan","family":"Chatterjee","sequence":"first","affiliation":[{"name":"Department of Computer Science & Engineering, Nalla Malla Reddy Engineering College, Hyderabad 500088, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ramagiri Praveen","family":"Kumar","sequence":"additional","affiliation":[{"name":"Department of Computer Science & Engineering, Nalla Malla Reddy Engineering College, Hyderabad 500088, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7670-2269","authenticated-orcid":false,"given":"Anjan","family":"Bandyopadhyay","sequence":"additional","affiliation":[{"name":"School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar 751024, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7089-1863","authenticated-orcid":false,"given":"Sujata","family":"Swain","sequence":"additional","affiliation":[{"name":"School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar 751024, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4107-6784","authenticated-orcid":false,"given":"Saurav","family":"Mallik","sequence":"additional","affiliation":[{"name":"Department of Environmental Health, Harvard T H Chan School of Public Health, Boston, MA 02115, USA"},{"name":"Department of Pharmacology & Toxicology, The University of Arizona, Tucson, MA 85721, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aimin","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Xi\u2019an University of Technology, Xi\u2019an 710048, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kanad","family":"Ray","sequence":"additional","affiliation":[{"name":"Amity School of Applied Sciences, Amity University Rajasthan, Jaipur 303002, India"},{"name":"Facultad de CienciasFisico-Matematicas, Benem\u00e9rita Universidad Aut\u00f3noma de Puebla, Av. San Claudio y AV. 18 sur, Col. San Manuel Ciudad Universitaria, Pueble Pue 72570, Mexico"},{"name":"Faubert Lab, Ecole d\u2019optom\u00e9trie, Universit\u00e9 de Montr\u00e9al, Montr\u00e9al, QC H3T1P1, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.ijmedinf.2018.09.008","article-title":"Early detection of Parkinson\u2019s disease through patient questionnaire and predictive modelling","volume":"119","author":"Prashanth","year":"2018","journal-title":"Int. J. Med. Inform."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.pneurobio.2006.11.009","article-title":"Advances in the treatment of Parkinson\u2019s disease","volume":"81","author":"Singh","year":"2007","journal-title":"Prog. 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