{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T17:52:55Z","timestamp":1755798775009,"version":"3.37.3"},"reference-count":30,"publisher":"European Alliance for Innovation n.o.","license":[{"start":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T00:00:00Z","timestamp":1695254400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-sa\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["EAI Endorsed Trans Perv Health Tech"],"abstract":"<jats:p>Parkinson's Disease (PD) is a persistent neurological condition that has a global impact on a significant number of individuals. The timely detection of PD is imperative for the efficacious treatment and control of the condition. Machine learning (ML) methods have demonstrated significant potential in forecasting Parkinson's disease (PD) based on diverse data sources in recent times. The present research paper outlines a study that employs machine learning [ML]techniques to predict Parkinson's disease. A dataset comprising clinical and demographic characteristics of both patients diagnosed with PD and healthy individuals was taken from Kaggle. The aforementioned dataset was utilized to train and assess multiple machine learning models. The experimental findings indicate that the CatBoost\u00a0model exhibited superior performance compared to the other models, achieving an accuracy rate of 95.1% and a root mean squared error of\u00a0of 0.34.In summary, our research showcases the capabilities of machine learning methodologies in forecasting Parkinson's disease and offers valuable insights into the crucial predictors for PD prognosis. The results of our study could potentially contribute to the advancement of diagnostic methods for the timely identification of PD, with increased precision and efficacy.<\/jats:p>","DOI":"10.4108\/eetpht.9.3933","type":"journal-article","created":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T10:02:37Z","timestamp":1695290557000},"source":"Crossref","is-referenced-by-count":6,"title":["Prognoza: Parkinson\u2019s Disease Prediction Using Classification Algorithms"],"prefix":"10.4108","volume":"9","author":[{"given":"Mithun","family":"Shivakoti","sequence":"first","affiliation":[]},{"given":"Sai Charan","family":"Medaramatla","sequence":"additional","affiliation":[]},{"given":"Deepthi","family":"Godavarthi","sequence":"additional","affiliation":[]},{"given":"Narsaiah","family":"Shivakoti","sequence":"additional","affiliation":[]}],"member":"2587","published-online":{"date-parts":[[2023,9,21]]},"reference":[{"key":"34501","doi-asserted-by":"crossref","unstructured":"G. Nagasubramanian, M. Sankayya, F. Al-Turjman and G. Tsaramirsis, \"Parkinson Data Analysis and Prediction System Using Multi-Variant Stacked Auto Encoder,\" in IEEE Access, vol. 8, pp. 127004-127013, 2020, doi: 10.1109\/ACCESS.2020.3007140.","DOI":"10.1109\/ACCESS.2020.3007140"},{"key":"34502","doi-asserted-by":"crossref","unstructured":"L. Ramig, R. Sherer, I. Titze and S. Ringel, \u201cAcoustic Analysis of Voices of Patients with Neurologic Disease: Rationale and Preliminary Data,\u201d The Annals of Otology, Rhinology, and laryngology, No. 97, pp. 164-172, 1988.","DOI":"10.1177\/000348948809700214"},{"key":"34503","doi-asserted-by":"crossref","unstructured":"Hughes, A. J., Daniel, S. E., Kilford, L. & Lees, A. J. Accuracy of clinical diagnosis of idiopathic Parkinson\u2019s disease: a clinico-pathological study of 100 cases. J. Neurol. Neurosurg. Psychiatry 55, 181\u2013184 (1992).","DOI":"10.1136\/jnnp.55.3.181"},{"key":"34504","doi-asserted-by":"crossref","unstructured":"Postuma, R. B. et al. MDS clinical diagnostic criteria for Parkinson\u2019s disease. Mov. Disord. 30, 1591\u20131601 (2015).","DOI":"10.1002\/mds.26424"},{"key":"34505","doi-asserted-by":"crossref","unstructured":"Jankovic, J. et al. Variable expression of Parkinson\u2019s disease: a base\u2010line analysis of the DAT ATOP cohort. Neurology 40, 1529\u20131529 (1990).","DOI":"10.1212\/WNL.40.10.1529"},{"key":"34506","doi-asserted-by":"crossref","unstructured":"Zetusky, W. J., Jankovic, J. & Pirozzolo, F. J. The heterogeneity of Parkinson\u2019s disease: clinical and prognostic implications. Neurology 35, 522\u2013526 (1985)","DOI":"10.1212\/WNL.35.4.522"},{"key":"34507","doi-asserted-by":"crossref","unstructured":"Ho, A. K., Iansek, R., Marigliani, C., Bradshaw, J. L. & Gates, S. Speech impairment in a large sample of patients with parkinson\u2019s disease. Behavioural neurology 11, 131\u2013137 (1999).","DOI":"10.1155\/1999\/327643"},{"key":"34508","doi-asserted-by":"crossref","unstructured":"Chen, H.-L. et al. An efficient diagnosis system for detection of parkinson\u2019s disease using fuzzy k-nearest neighbor approach. Expert Systems with Applications 40, 263\u2013271 (2013).","DOI":"10.1016\/j.eswa.2012.07.014"},{"key":"34509","doi-asserted-by":"crossref","unstructured":"Prashanth, R., Dutta Roy, S., Mandal, P. K. & Ghosh, S. High-accuracy detection of early Parkinson\u2019s disease through multimodal features and machine learning. Int. J. Med. Inform. 90, 13\u201321 (2016).","DOI":"10.1016\/j.ijmedinf.2016.03.001"},{"key":"34510","doi-asserted-by":"crossref","unstructured":"Lee, D. A., Lee, H.-J., Kim, H. C. & Park, K. M. Application of machine learning analysis based on diffusion tensor imaging to identify REM sleep behavior disorder. Sleep Breath. https:\/\/doi.org\/10.1007\/s11325-021-02434-9 (2021).","DOI":"10.1007\/s11325-021-02434-9"},{"key":"34511","doi-asserted-by":"crossref","unstructured":"Mei, J. et al. Identification of REM sleep behavior disorder by structural magnetic resonance imaging and machine learning. Preprint at bioRxiv https:\/\/doi.org\/ 10.1101\/2021.09.18.21263779 (2021).","DOI":"10.1101\/2021.09.18.21263779"},{"key":"34512","doi-asserted-by":"crossref","unstructured":"Chen-Plotkin, A. S. Parkinson disease: blood transcriptomics for Parkinson disease? Nat. Rev. Neurol. 14, 5\u20136 (2018).","DOI":"10.1038\/nrneurol.2017.166"},{"key":"34513","doi-asserted-by":"crossref","unstructured":"Uehara, Y. et al. Non-invasive diagnostic tool for Parkinson\u2019s disease by sebum RNA profile with machine learning. Sci. Rep. 11, 18550 (2021).","DOI":"10.1038\/s41598-021-98423-9"},{"key":"34514","doi-asserted-by":"crossref","unstructured":"Noyce, A. J. et al. PREDICT-PD: identifying risk of Parkinson\u2019s disease in the community: methods and baseline results. J. Neurol. Neurosurg. Psychiatry 85, 31\u201337 (2014).","DOI":"10.1136\/jnnp-2013-305420"},{"key":"34515","doi-asserted-by":"crossref","unstructured":"Palmerini, L. et al. Identification of characteristic motor patterns preceding freezing of gait in Parkinson\u2019s disease using wearable sensors. Front. Neurol. 8, 394 (2017).","DOI":"10.3389\/fneur.2017.00394"},{"key":"34516","doi-asserted-by":"crossref","unstructured":"Paulsen, J. S. et al. A review of quality of life after predictive testing for and earlier identification of neurodegenerative diseases. Prog. Neurobiol. 110, 2\u201328 (2013).","DOI":"10.1016\/j.pneurobio.2013.08.003"},{"key":"34517","doi-asserted-by":"crossref","unstructured":"Das, R. A comparison of multiple classification methods for diagnosis of parkinson disease. Expert Systems with Applications 37, 1568\u20131572 (2010).","DOI":"10.1016\/j.eswa.2009.06.040"},{"key":"34518","doi-asserted-by":"crossref","unstructured":"Bhattacharya, I. & Bhatia, M. Svm classification to distinguish parkinson disease patients. In Proceedings of the 1st Amrita ACM-W Celebration on Women in Computing in India, 14 (ACM, 2010).","DOI":"10.1145\/1858378.1858392"},{"key":"34519","doi-asserted-by":"crossref","unstructured":"Chen, H.-L. et al. An efficient diagnosis system for detection of parkinson\u2019s disease using fuzzy k-nearest neighbor approach. Expert Systems with Applications 40, 263\u2013271 (2013).","DOI":"10.1016\/j.eswa.2012.07.014"},{"key":"34520","doi-asserted-by":"crossref","unstructured":"Ozcift, A. Svm feature selection-based rotation forest ensemble classifiers to improve computer-aided diagnosis of parkinson disease. Journal of medical systems 36, 2141\u20132147 (2012).","DOI":"10.1007\/s10916-011-9678-1"},{"key":"34521","doi-asserted-by":"crossref","unstructured":"Hariharan, M., Polat, K. & Sindhu, R. A new hybrid intelligent system for accurate detection of parkinson\u2019s disease. Computer methods and programs in biomedicine 113, 904\u2013913 (2014).","DOI":"10.1016\/j.cmpb.2014.01.004"},{"key":"34522","doi-asserted-by":"crossref","unstructured":"Froelich, W., Wrobel, K. & Porwik, P. Diagnosis of parkinson\u2019s disease using speech samples and threshold-based classification. Journal of Medical Imaging and Health Informatics 5, 1358\u20131363 (2015).","DOI":"10.1166\/jmihi.2015.1539"},{"key":"34523","doi-asserted-by":"crossref","unstructured":"Eskidere, \u00d6., Ertas, F. & Hanil\u00e7i, C. A comparison of regression methods for remote tracking of parkinson\u2019s disease progression. Expert Systems with Applications 39, 5523\u20135528 (2012).","DOI":"10.1016\/j.eswa.2011.11.067"},{"key":"34524","doi-asserted-by":"crossref","unstructured":"Guo, J.-F. et al. Polygenic determinants of parkinson\u2019s disease in a chinese population. Neurobiology of aging 36, 1765\u2013e1 (2015).","DOI":"10.1016\/j.neurobiolaging.2014.12.030"},{"key":"34525","doi-asserted-by":"crossref","unstructured":"Polat, K. Classification of parkinson\u2019s disease using feature weighting method on the basis of fuzzy c-means clustering. International Journal of Systems Science 43, 597\u2013609 (2012).","DOI":"10.1080\/00207721.2011.581395"},{"key":"34526","doi-asserted-by":"crossref","unstructured":"\u00c5str\u00f6m, F. & Koker, R. A parallel neural network approach to prediction of parkinson\u2019s disease. Expert systems with applications 38, 12470\u201312474 (2011).","DOI":"10.1016\/j.eswa.2011.04.028"},{"key":"34527","unstructured":"DIPAYAN BISWAS, (2019). Parkinson\u2019s Disease (PD) classification, Version 1, Retrieved March 15, 2023, from https:\/\/www.kaggle.com\/datasets\/dipayanbiswas\/parkinsons-disease-speech-signal-features."},{"key":"34528","doi-asserted-by":"crossref","unstructured":"Shivakoti, M., Jeeveth, K., Pradhan, N.R., Yesu Babu, M. (2023). Apple Stock Price Prediction Using Regression Techniques. In: Balas, V.E., Semwal, V.B., Khandare, A. (eds) Intelligent Computing and Networking. IC-ICN 2023. Lecture Notes in Networks and Systems, vol 699. Springer, Singapore. https:\/\/doi.org\/10.1007\/978-981-99-3177-4_5","DOI":"10.1007\/978-981-99-3177-4_5"},{"key":"34529","doi-asserted-by":"crossref","unstructured":"CMBA-SVM: a clinical approach for Parkinson disease diagnosis, Bibhuprasad Sahu & Sachi Nandan Mohanty, International Journal of Information and Technology, 13(3), 647-655, (2021), doI: 10.1007\/s41870-020-00569-8, ISSN: 2511-2104","DOI":"10.1007\/s41870-020-00569-8"},{"key":"34530","doi-asserted-by":"crossref","unstructured":"Shivakoti Mithun, Srinivasa Reddy K, and Adinarayana Reddy. \u201cAn Efficient Regression Method To Predict Soil pH Using RGB Values.\u201d International Research Journal on Advanced Science Hub 05 .05S May (2023): 35\u201342. http:\/\/dx.doi.org\/10.47392\/irjash.2023.S005","DOI":"10.47392\/irjash.2023.S005"}],"container-title":["EAI Endorsed Transactions on Pervasive Health and Technology"],"original-title":[],"link":[{"URL":"https:\/\/publications.eai.eu\/index.php\/phat\/article\/download\/3933\/2531","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/publications.eai.eu\/index.php\/phat\/article\/download\/3933\/2531","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,21]],"date-time":"2024-09-21T17:07:53Z","timestamp":1726938473000},"score":1,"resource":{"primary":{"URL":"https:\/\/publications.eai.eu\/index.php\/phat\/article\/view\/3933"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,21]]},"references-count":30,"URL":"https:\/\/doi.org\/10.4108\/eetpht.9.3933","relation":{},"ISSN":["2411-7145"],"issn-type":[{"type":"electronic","value":"2411-7145"}],"subject":[],"published":{"date-parts":[[2023,9,21]]}}}