{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T16:00:30Z","timestamp":1764259230258,"version":"3.46.0"},"reference-count":28,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T00:00:00Z","timestamp":1764201600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T00:00:00Z","timestamp":1764201600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Multimedia University, Malaysia","award":["MMUI\/240092","MMUI\/240092","MMUI\/240092"],"award-info":[{"award-number":["MMUI\/240092","MMUI\/240092","MMUI\/240092"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Intell Syst"],"DOI":"10.1007\/s44196-025-01053-6","type":"journal-article","created":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T15:57:41Z","timestamp":1764259061000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Uncertainty-Aware Boosting Ensemble for Parkinson\u2019s Disease Early Detection"],"prefix":"10.1007","volume":"18","author":[{"given":"Mahmoud E.","family":"Farfoura","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmad A. A.","family":"Alkhatib","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tee","family":"Connie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,11,27]]},"reference":[{"issue":"3","key":"1053_CR1","first-page":"223","volume":"10","author":"H Luo","year":"2025","unstructured":"Luo, H., Wang, Y., Zhan, M., Li, Z., Han, Y.: Global, regional, and national burden of Parkinson\u2019s disease, 1990\u20132021: a systematic analysis for the global burden of disease study 2021. Lancet Public Health 10(3), 223\u2013234 (2025)","journal-title":"Lancet Public Health"},{"key":"1053_CR2","unstructured":"\u201cParkinson disease,\u201d World Health Organization, 2024. [Online]. Available: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/parkinson-disease"},{"key":"1053_CR3","unstructured":"Horton, M.: AI advances Parkinson\u2019s detection using standard MRI scans, NVIDIA Technical Blog, Apr. 11, 2025. [Online]. Available: https:\/\/developer.nvidia.com\/blog\/ai-advances-parkinsons-detection-using-standard-mri-scans."},{"key":"1053_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3389\/frai.2023.1084001","volume":"6","author":"R Alshammri","year":"2023","unstructured":"Alshammri, R., Alharbi, G., Alharbi, E., Almubark, I.: Machine learning approaches to identify Parkinson\u2019s disease using voice signal features. Front Artif Intell 6, 1\u201310 (2023). https:\/\/doi.org\/10.3389\/frai.2023.1084001","journal-title":"Front Artif Intell"},{"key":"1053_CR5","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2023.1081087","volume":"10","author":"K Soman","year":"2023","unstructured":"Soman, K., Nelson, C.A., Cerono, G., Goldman, S.M., Baranzini, S.E., Brown, E.G.: Early detection of Parkinson\u2019s disease through enriching the electronic health record using a biomedical knowledge graph. Front. Med. 10, 1081087 (2023). https:\/\/doi.org\/10.3389\/fmed.2023.1081087","journal-title":"Front. Med."},{"key":"1053_CR6","unstructured":"\"Parkinson\u2019s disease: Advances in diagnosis and treatment, MedCentral. [Online]. Available: https:\/\/www.medcentral.com\/neurology\/parkinsons-disease-advances-in-diagnosis-and-treatment. [Accessed: 17-06-2025]."},{"key":"1053_CR7","doi-asserted-by":"publisher","unstructured":"Athamneh, A. A., Farfoura, M. E., Rosiyadi, D.: A CatBoost predictive model for parkinson\u2019s disease early detection, In: 2025 1st International conference on computational intelligence approaches and applications (ICCIAA), Amman, Jordan, 2025, pp. 1\u20136, https:\/\/doi.org\/10.1109\/ICCIAA65327.2025.11013735.","DOI":"10.1109\/ICCIAA65327.2025.11013735"},{"issue":"2","key":"1053_CR8","doi-asserted-by":"publisher","DOI":"10.3390\/fi15020085","volume":"15","author":"S AlZu\u2019bi","year":"2023","unstructured":"AlZu\u2019bi, S., Elbes, M., Mughaid, A., Bdair, N., Abualigah, L., Forestiero, A., Zitar, R.A.: Diabetes monitoring system in smart health cities based on big data intelligence. Future Internet 15(2), 85 (2023). https:\/\/doi.org\/10.3390\/fi15020085","journal-title":"Future Internet"},{"key":"1053_CR9","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-023-02663-5","author":"C Morgan","year":"2023","unstructured":"Morgan, C., Tonkin, E.L., Masullo, A., Jovan, F., Sikdar, A., Khaire, P., Mirmehdi, M., McConville, R., Tourte, G.J.L., Whone, A., Craddock, I.: A multimodal dataset of real world mobility activities in Parkinson\u2019s disease. Sci. Data (2023). https:\/\/doi.org\/10.1038\/s41597-023-02663-5","journal-title":"Sci. Data"},{"key":"1053_CR10","doi-asserted-by":"crossref","unstructured":"Sarraf, S., Tofighi, G.: DeepAD: Alzheimer\u2019s disease classification via deep convolutional neural networks using MRI and fMRI, bioRxiv, 2016.","DOI":"10.1101\/070441"},{"issue":"1","key":"1053_CR11","first-page":"153","volume":"9","author":"J Rodr\u00edguez-Molinero","year":"2023","unstructured":"Rodr\u00edguez-Molinero, J., et al.: Overview on wearable sensors for the management of Parkinson\u2019s disease. NPJ Digit. Med. 9(1), 153 (2023)","journal-title":"NPJ Digit. Med."},{"key":"1053_CR12","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0183989","author":"JCM Schlachetzki","year":"2017","unstructured":"Schlachetzki, J.C.M., Barth, J., Marxreiter, F., Gossler, J., Kohl, Z., Reinfelder, S., Gassner, H., Aminian, K., Eskofier, B.M., Winkler, J., Klucken, J.: Wearable sensors objectively measure gait parameters in Parkinson\u2019s disease. PLoS ONE (2017). https:\/\/doi.org\/10.1371\/journal.pone.0183989","journal-title":"PLoS ONE"},{"issue":"1","key":"1053_CR13","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1109\/JBHI.2019.2898332","volume":"24","author":"S Aghanavesi","year":"2019","unstructured":"Aghanavesi, S., Bergquist, F., Nyholm, D., Senek, M., Memedi, M.: Motion sensor-based assessment of Parkinsons disease motor symptoms during leg agility tests: results from levodopa challenge. IEEE J Biomed Health Inf 24(1), 111\u2013119 (2019)","journal-title":"IEEE J Biomed Health Inf"},{"issue":"1","key":"1053_CR14","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1109\/JBHI.2019.2903627","volume":"24","author":"M Ricci","year":"2019","unstructured":"Ricci, M., Lazzaro, G.D., Pisani, A., Mercuri, N.B., Giannini, F., Saggio, G.: Assessment of motor impairments in early untreated Parkinsons disease patients: the wearable electronics impact. IEEE J Biomed Health Inf 24(1), 120\u2013130 (2019)","journal-title":"IEEE J Biomed Health Inf"},{"issue":"4","key":"1053_CR15","doi-asserted-by":"publisher","first-page":"1015","DOI":"10.1109\/TBME.2008.2005954","volume":"56","author":"MA Little","year":"2009","unstructured":"Little, M.A., McSharry, P.E., Hunter, E.J., Spielman, J., Ramig, L.O.: Suitability of dysphonia measurements for telemonitoring of Parkinson\u2019s disease. IEEE Trans. Biomed. Eng. 56(4), 1015\u20131022 (2009). https:\/\/doi.org\/10.1109\/TBME.2008.2005954","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"2","key":"1053_CR16","first-page":"33","volume":"3","author":"AK Tiwari","year":"2016","unstructured":"Tiwari, A.K.: Machine learning based approaches for prediction of Parkinson\u2019s disease. Mach Learn Appl: Int J 3(2), 33\u201339 (2016)","journal-title":"Mach Learn Appl: Int J"},{"key":"1053_CR17","doi-asserted-by":"publisher","first-page":"1783","DOI":"10.1016\/j.procs.2025.04.133","volume":"259","author":"A Bashir","year":"2025","unstructured":"Bashir, A., Singh, Y., Zadoo, S., Mir, K.H.: Parkinson\u2019s disease detection: a machine learning based model. Proced Comput. Sci. 259, 1783\u20131792 (2025). https:\/\/doi.org\/10.1016\/j.procs.2025.04.133","journal-title":"Proced Comput. Sci."},{"key":"1053_CR18","doi-asserted-by":"publisher","first-page":"147","DOI":"10.4236\/jbise.2014.74019","volume":"7","author":"M Shahbakhi","year":"2014","unstructured":"Shahbakhi, M., Far, D., Tahami, E.: Speech analysis for diagnosis of Parkinson\u2019s disease using genetic algorithm and support vector machine. J. Biomed. Sci. Eng. 7, 147\u2013156 (2014). https:\/\/doi.org\/10.4236\/jbise.2014.74019","journal-title":"J. Biomed. Sci. Eng."},{"key":"1053_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113562","volume":"159","author":"M Nilashi","year":"2020","unstructured":"Nilashi, M., Ahmadi, H., Sheikhtaheri, A., Naemi, R., Alotaibi, R., Alarood, A.A., Munshi, A., Rashid, T.A., Zhao, J.: Remote tracking of Parkinson\u2019s disease progression using ensembles of deep belief network and self organizing map. Expert Syst. Appl. 159, 113562 (2020). https:\/\/doi.org\/10.1016\/j.eswa.2020.113562","journal-title":"Expert Syst. Appl."},{"key":"1053_CR20","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-024-01236-z","volume":"7","author":"C Brzenczek","year":"2024","unstructured":"Brzenczek, C., et al.: Integrating digital gait data with metabolomics and clinical data to predict outcomes in Parkinson\u2019s disease. NPJ Digit. Med. 7, 235 (2024). https:\/\/doi.org\/10.1038\/s41746-024-01236-z","journal-title":"NPJ Digit. Med."},{"key":"1053_CR21","doi-asserted-by":"publisher","first-page":"20089","DOI":"10.1007\/s00521-024-10299-8","volume":"36","author":"Y Do\u011fan","year":"2024","unstructured":"Do\u011fan, Y.: An innovative approach for Parkinson\u2019s disease diagnosis using CNN, NCA, and SVM. Neural Comput. Appl. 36, 20089\u201320110 (2024). https:\/\/doi.org\/10.1007\/s00521-024-10299-8","journal-title":"Neural Comput. Appl."},{"key":"1053_CR22","doi-asserted-by":"publisher","unstructured":"Chatterjee, J., Saxena, A., Vyas, G., Mehra, A: A computer vision approach to diagnose Parkinson disease using brain CT images In: 2018 Second international conference on computing methodologies and communication (ICCMC), 2018, pp.\u202f463\u2013467, https:\/\/doi.org\/10.1109\/ICCMC.2018.8488034.","DOI":"10.1109\/ICCMC.2018.8488034"},{"key":"1053_CR23","doi-asserted-by":"publisher","unstructured":"Jahan, N., Nesa, A., Layek, M. A.: Parkinson\u2019s disease detection using CNN architectures with transfer learning, In 2021 Int. conf. on innovative computing, intelligent communication and smart electrical systems (ICSES), Chennai, India, 2021, pp.\u202f1\u20135, https:\/\/doi.org\/10.1109\/ICSES52305.2021.9633872.","DOI":"10.1109\/ICSES52305.2021.9633872"},{"issue":"1","key":"1053_CR24","first-page":"113","volume":"15","author":"HA Shehadeh","year":"2023","unstructured":"Shehadeh, H.A., Jebril, I.H., Jaradat, G.M., Ibrahim, D., Sihwail, R., Al Hamad, H., Chu, S.-C., Alia, M.A.: Intelligent diagnostic prediction and classification system for Parkinson\u2019s disease by incorporating sperm swarm optimization (SSO) and density-based feature selection methods. Int. J. Adv. Soft Comput. Appl. 15(1), 113\u2013132 (2023)","journal-title":"Int. J. Adv. Soft Comput. Appl."},{"issue":"1","key":"1053_CR25","first-page":"196","volume":"14","author":"M Elbes","year":"2022","unstructured":"Elbes, M., Kanan, T., Alia, M., Ziad, M.: COVID-19 detection platform from X-ray images using deep learning. Int. J. Adv. Soft Comput. Appl. 14(1), 196\u2013211 (2022)","journal-title":"Int. J. Adv. Soft Comput. Appl."},{"key":"1053_CR26","unstructured":"Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., Liu, T.-Y.: LightGBM: a highly efficient gradient boosting decision tree. In Advances in Neural Information Processing Systems, vol. 30, 2017."},{"key":"1053_CR27","unstructured":"Optuna, \u201cOptuna: a next-generation hyperparameter optimization framework, [Online]. Available: https:\/\/optuna.org\/. [Accessed: 06\u201302\u20132025]."},{"issue":"11","key":"1053_CR28","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1080\/14786440109462720","volume":"2","author":"K Pearson","year":"1901","unstructured":"Pearson, K.: On lines and planes of closest fit to systems of points in space. Philos. Mag. 2(11), 559\u2013572 (1901)","journal-title":"Philos. Mag."}],"container-title":["International Journal of Computational Intelligence Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-025-01053-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44196-025-01053-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-025-01053-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T15:57:46Z","timestamp":1764259066000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44196-025-01053-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,27]]},"references-count":28,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1053"],"URL":"https:\/\/doi.org\/10.1007\/s44196-025-01053-6","relation":{},"ISSN":["1875-6883"],"issn-type":[{"value":"1875-6883","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,27]]},"assertion":[{"value":"21 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 August 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 October 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 November 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This research was conducted in accordance with ethical standards. All data used in this study were collected from publicly available sources, and no personal or sensitive information was used. Informed consent was not applicable as the data were anonymized and de-identified before use, ensuring the privacy and confidentiality of individuals. The study adhered to the principles of the Declaration of Helsinki and relevant institutional guidelines.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval and Informed Consent"}},{"value":"We affirm that all authors have agreed to the submission and publication of this paper. Furthermore, we acknowledge that we have appropriately cited all sources and obtained necessary permissions for any copyrighted material included in the manuscript.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}],"article-number":"313"}}