{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T16:53:49Z","timestamp":1792601629975,"version":"4.5.0"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T00:00:00Z","timestamp":1771459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T00:00:00Z","timestamp":1771459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cogn Neurodyn"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1007\/s11571-026-10426-2","type":"journal-article","created":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T09:26:10Z","timestamp":1771493170000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Predictive modeling of vocal biomarkers for the diagnosis of Parkinson\u2019s disease"],"prefix":"10.1007","volume":"20","author":[{"given":"Declan Ikechukwu","family":"Emegano","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mubarak Taiwo","family":"Mustapha","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emeje Paul","family":"Isaac","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ilker","family":"Ozsahin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Berna","family":"Uzun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dilber Uzun","family":"Ozsahin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,2,19]]},"reference":[{"key":"10426_CR1","doi-asserted-by":"publisher","first-page":"17013","DOI":"10.1038\/nrdp.2017.13","volume":"3","author":"W Poewe","year":"2017","unstructured":"Poewe W, Seppi K, Tanner CM et al (2017) Parkinson disease. Nat Rev Dis Primers 3:17013. https:\/\/doi.org\/10.1038\/nrdp.2017.13","journal-title":"Nat Rev Dis Primers"},{"key":"10426_CR2","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1111\/ene.14108","volume":"27","author":"R Balestrino","year":"2020","unstructured":"Balestrino R, Schapira AHV (2020) Parkinson disease. Euro J Neurol 27:27\u201342. https:\/\/doi.org\/10.1111\/ene.14108","journal-title":"Euro J Neurol"},{"key":"10426_CR3","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1016\/S1474-4422(23)00109-6","volume":"22","author":"A Siderowf","year":"2023","unstructured":"Siderowf A, Concha-Marambio L, Lafontant D-E et al (2023) Assessment of heterogeneity among participants in the parkinson\u2019s progression markers initiative cohort using \u03b1-synuclein seed amplification: a cross-sectional study. Lancet Neurol 22:407\u2013417. https:\/\/doi.org\/10.1016\/S1474-4422(23)00109-6","journal-title":"Lancet Neurol"},{"key":"10426_CR4","doi-asserted-by":"publisher","unstructured":"Tolosa E, Scholz SW (2021) Assessment of heterogeneity among participants in the Parkinson\u2019s Progression Markers Initiative cohort using \u03b1-synuclein seed amplification: a cross-sectional study Siderowf, Andrew The Lancet Neurology, Volume 22, Issue 5, 407\u2013417. The Lancet Neurology 20:385\u2013397. https:\/\/doi.org\/10.1016\/S1474-4422(21)00030-2","DOI":"10.1016\/S1474-4422(21)00030-2"},{"key":"10426_CR5","doi-asserted-by":"publisher","first-page":"548","DOI":"10.1001\/jama.2019.22360","volume":"323","author":"MJ Armstrong","year":"2020","unstructured":"Armstrong MJ, Okun MS (2020) Diagnosis and treatment of Parkinson disease: A review. JAMA 323:548. https:\/\/doi.org\/10.1001\/jama.2019.22360","journal-title":"JAMA"},{"key":"10426_CR6","doi-asserted-by":"publisher","first-page":"107026","DOI":"10.1016\/j.parkreldis.2024.107026","volume":"125","author":"N Grotewold","year":"2024","unstructured":"Grotewold N, Albin RL (2024) Update: protective and risk factors for Parkinson disease. Parkinsonism Relat Disord 125:107026. https:\/\/doi.org\/10.1016\/j.parkreldis.2024.107026","journal-title":"Parkinsonism Relat Disord"},{"key":"10426_CR7","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1016\/S1474-4422(19)30287-X","volume":"19","author":"C Blauwendraat","year":"2020","unstructured":"Blauwendraat C, Nalls MA, Singleton AB (2020) The genetic architecture of parkinson\u2019s disease. Lancet Neurol 19:170\u2013178. https:\/\/doi.org\/10.1016\/S1474-4422(19)30287-X","journal-title":"Lancet Neurol"},{"key":"10426_CR8","doi-asserted-by":"publisher","first-page":"100074","DOI":"10.1016\/j.bionps.2023.100074","volume":"9","author":"KY Yamashita","year":"2023","unstructured":"Yamashita KY, Bhoopatiraju S, Silverglate BD, Grossberg GT (2023) Biomarkers in parkinson\u2019s disease: A state of the Art review. Biomarkers Neuropsychiatry 9:100074. https:\/\/doi.org\/10.1016\/j.bionps.2023.100074","journal-title":"Biomarkers Neuropsychiatry"},{"key":"10426_CR9","doi-asserted-by":"publisher","first-page":"566","DOI":"10.1212\/WNL.0000000000002350","volume":"86","author":"G Rizzo","year":"2016","unstructured":"Rizzo G, Copetti M, Arcuti S et al (2016) Accuracy of clinical diagnosis of Parkinson disease: A systematic review and meta-analysis. Neurology 86:566\u2013576. https:\/\/doi.org\/10.1212\/WNL.0000000000002350","journal-title":"Neurology"},{"key":"10426_CR10","doi-asserted-by":"publisher","first-page":"1201","DOI":"10.1007\/s00702-022-02498-1","volume":"129","author":"L T\u00f6nges","year":"2022","unstructured":"T\u00f6nges L, Buhmann C, Klebe S et al (2022) Blood-based biomarker in parkinson\u2019s disease: potential for future applications in clinical research and practice. J Neural Transm 129:1201\u20131217. https:\/\/doi.org\/10.1007\/s00702-022-02498-1","journal-title":"J Neural Transm"},{"key":"10426_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvoice.2024.11.008","author":"N Madusanka","year":"2024","unstructured":"Madusanka N, Lee B (2024) Vocal biomarkers for parkinson\u2019s disease classification using audio spectrogram Transformers. J Voice S0892199724003886. https:\/\/doi.org\/10.1016\/j.jvoice.2024.11.008","journal-title":"J Voice S0892199724003886"},{"key":"10426_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3433180","volume":"17","author":"MA Mohammed","year":"2021","unstructured":"Mohammed MA, Elhoseny M, Abdulkareem KH et al (2021) A Multi-agent feature selection and hybrid classification model for parkinson\u2019s disease diagnosis. ACM Trans Multimedia Comput Commun Appl 17:1\u201322. https:\/\/doi.org\/10.1145\/3433180","journal-title":"ACM Trans Multimedia Comput Commun Appl"},{"key":"10426_CR13","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-024-20276-x","author":"A Ouhmida","year":"2024","unstructured":"Ouhmida A, Saleh S, Ammar A et al (2024) HEFS-MLDR: A novel hybrid ensemble feature selection framework for improved deep neural network architecture in the diagnosis of parkinson\u2019s disease. Multimed Tools Appl. https:\/\/doi.org\/10.1007\/s11042-024-20276-x","journal-title":"Multimed Tools Appl"},{"key":"10426_CR14","doi-asserted-by":"publisher","first-page":"38","DOI":"10.20473\/jisebi.10.1.38-50","volume":"10","author":"J Jumanto","year":"2024","unstructured":"Jumanto J, Rofik R, Sugiharti E et al (2024) Optimizing support vector machine performance for parkinson\u2019s disease diagnosis using GridSearchCV and PCA-Based feature extraction. J Inf Syst Eng Bus Intell 10:38\u201350. https:\/\/doi.org\/10.20473\/jisebi.10.1.38-50","journal-title":"J Inf Syst Eng Bus Intell"},{"key":"10426_CR15","doi-asserted-by":"publisher","first-page":"1575","DOI":"10.3390\/math12101575","volume":"12","author":"SNH Bukhari","year":"2024","unstructured":"Bukhari SNH, Ogudo KA (2024) Ensemble machine learning approach for parkinson\u2019s disease detection using speech signals. Mathematics 12:1575. https:\/\/doi.org\/10.3390\/math12101575","journal-title":"Mathematics"},{"key":"10426_CR16","doi-asserted-by":"publisher","first-page":"e46105","DOI":"10.2196\/46105","volume":"25","author":"A Idrisoglu","year":"2023","unstructured":"Idrisoglu A, Dallora AL, Anderberg P, Berglund JS (2023) Applied machine learning techniques to diagnose Voice-Affecting conditions and disorders: systematic literature review. J Med Internet Res 25:e46105. https:\/\/doi.org\/10.2196\/46105","journal-title":"J Med Internet Res"},{"key":"10426_CR17","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1504\/IJISTA.2023.131576","volume":"21","author":"JC Saldanha","year":"2023","unstructured":"Saldanha JC, Suvarna M, Satish D, Santhmayor C (2023) Jitter as a quantitative indicator of dysphonia in parkinson\u2019s disease. IJISTA 21:199\u2013228. https:\/\/doi.org\/10.1504\/IJISTA.2023.131576","journal-title":"IJISTA"},{"key":"10426_CR18","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1007\/s11910-021-01096-0","volume":"21","author":"MR Rafferty","year":"2021","unstructured":"Rafferty MR, Nettnin E, Goldman JG, MacDonald J (2021) Frameworks for parkinson\u2019s disease rehabilitation addressing When, What, and how. Curr Neurol Neurosci Rep 21:12. https:\/\/doi.org\/10.1007\/s11910-021-01096-0","journal-title":"Curr Neurol Neurosci Rep"},{"key":"10426_CR19","doi-asserted-by":"publisher","first-page":"11085","DOI":"10.3390\/app142311085","volume":"14","author":"E Majda-Zdancewicz","year":"2024","unstructured":"Majda-Zdancewicz E, Potulska-Chromik A, Nojszewska M, Kostera-Pruszczyk A (2024) Speech signal analysis in patients with parkinson\u2019s Disease, taking into account Phonation, Articulation, and prosody of speech. Appl Sci 14:11085. https:\/\/doi.org\/10.3390\/app142311085","journal-title":"Appl Sci"},{"key":"10426_CR20","doi-asserted-by":"publisher","first-page":"1796","DOI":"10.3390\/diagnostics12081796","volume":"12","author":"TS Mian","year":"2022","unstructured":"Mian TS (2022) An unsupervised neural network feature selection and 1D Convolution neural network classification for screening of parkinsonism. Diagnostics 12:1796. https:\/\/doi.org\/10.3390\/diagnostics12081796","journal-title":"Diagnostics"},{"key":"10426_CR21","doi-asserted-by":"crossref","unstructured":"SrinivasaRao N, Anusha D, Mayuri U, Eswar S (2023) An Effective Machine Learning Techniques to Detect Parkinson\u2019s Disease. In: 2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS). IEEE, Coimbatore, India, pp 564\u2013568","DOI":"10.1109\/ICAIS56108.2023.10073685"},{"key":"10426_CR22","doi-asserted-by":"crossref","unstructured":"Bian Z (2023) EEG-Based PD classification model coupled with machine learning. In: Yu Y, Nguyen BP, Sang J (eds) Studies in health technology and informatics. IOS","DOI":"10.3233\/SHTI230855"},{"key":"10426_CR23","doi-asserted-by":"crossref","unstructured":"Gupta I, Vashisth D, Garg R Performance Comparison of Machine Learning Algorithms for Parkinson\u2019s Disease Detection. In: 2024 14th International Conference on Cloud Computing, Data Science, Engineering, Confluence (2024) IEEE, Noida, India, pp 902\u2013907","DOI":"10.1109\/Confluence60223.2024.10463297"},{"key":"10426_CR24","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1186\/s12938-024-01265-5","volume":"23","author":"S Chen","year":"2024","unstructured":"Chen S, Shi Y, Wan L et al (2024) Attention-enhanced dilated Convolution for parkinson\u2019s disease detection using transcranial sonography. BioMed Eng OnLine 23:76. https:\/\/doi.org\/10.1186\/s12938-024-01265-5","journal-title":"BioMed Eng OnLine"},{"key":"10426_CR25","doi-asserted-by":"publisher","first-page":"106904","DOI":"10.1016\/j.epsr.2020.106904","volume":"192","author":"SK Gunturi","year":"2021","unstructured":"Gunturi SK, Sarkar D (2021) Ensemble machine learning models for the detection of energy theft. Electr Power Syst Res 192:106904. https:\/\/doi.org\/10.1016\/j.epsr.2020.106904","journal-title":"Electr Power Syst Res"},{"key":"10426_CR26","doi-asserted-by":"crossref","unstructured":"Sharifmoghadam M, Jazayeriy H (2019) Breast Cancer Classification Using AdaBoost- Extreme Learning Machine. In: 2019 5th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS). IEEE, Shahrood, Iran, pp 1\u20135","DOI":"10.1109\/ICSPIS48872.2019.9066088"},{"key":"10426_CR27","doi-asserted-by":"publisher","first-page":"2426377","DOI":"10.1080\/08839514.2024.2426377","volume":"38","author":"B Luna-Benoso","year":"2024","unstructured":"Luna-Benoso B, Mart\u00ednez-Perales JC, Morales-Rodr\u00edguez \u00daS et al (2024) A new classification model using a decision tree generated from hyperplanes in dimensional space. Appl Artif Intell 38:2426377. https:\/\/doi.org\/10.1080\/08839514.2024.2426377","journal-title":"Appl Artif Intell"},{"key":"10426_CR28","doi-asserted-by":"publisher","first-page":"108980","DOI":"10.1016\/j.ress.2022.108980","volume":"231","author":"Z Chen","year":"2023","unstructured":"Chen Z, Li X, Wang W et al (2023) Residual strength prediction of corroded pipelines using multilayer perceptron and modified feedforward neural network. Reliab Eng Syst Saf 231:108980. https:\/\/doi.org\/10.1016\/j.ress.2022.108980","journal-title":"Reliab Eng Syst Saf"},{"key":"10426_CR29","doi-asserted-by":"publisher","first-page":"109244","DOI":"10.1016\/j.petrol.2021.109244","volume":"208","author":"DA Otchere","year":"2022","unstructured":"Otchere DA, Ganat TOA, Ojero JO et al (2022) Application of gradient boosting regression model for the evaluation of feature selection techniques in improving reservoir characterisation predictions. J Petrol Sci Eng 208:109244. https:\/\/doi.org\/10.1016\/j.petrol.2021.109244","journal-title":"J Petrol Sci Eng"},{"key":"10426_CR30","doi-asserted-by":"publisher","first-page":"3034","DOI":"10.1007\/s10278-024-01172-0","volume":"37","author":"W Wang","year":"2024","unstructured":"Wang W, Sheng R, Liao S et al (2024) LightGBM is an effective predictive model for postoperative complications in gastric cancer: A study integrating radiomics with ensemble learning. J Digit Imaging Inf Med 37:3034\u20133048. https:\/\/doi.org\/10.1007\/s10278-024-01172-0","journal-title":"J Digit Imaging Inf Med"},{"key":"10426_CR31","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1186\/s40537-020-00369-8","volume":"7","author":"JT Hancock","year":"2020","unstructured":"Hancock JT, Khoshgoftaar TM (2020) CatBoost for big data: an interdisciplinary review. J Big Data 7:94. https:\/\/doi.org\/10.1186\/s40537-020-00369-8","journal-title":"J Big Data"},{"key":"10426_CR32","doi-asserted-by":"publisher","first-page":"1402384","DOI":"10.3389\/fdata.2024.1402384","volume":"7","author":"P Srisuradetchai","year":"2024","unstructured":"Srisuradetchai P, Suksrikran K (2024) Random kernel k-nearest neighbors regression. Front Big Data 7:1402384. https:\/\/doi.org\/10.3389\/fdata.2024.1402384","journal-title":"Front Big Data"}],"container-title":["Cognitive Neurodynamics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11571-026-10426-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11571-026-10426-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11571-026-10426-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T22:04:55Z","timestamp":1771538695000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11571-026-10426-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,19]]},"references-count":32,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,12]]}},"alternative-id":["10426"],"URL":"https:\/\/doi.org\/10.1007\/s11571-026-10426-2","relation":{},"ISSN":["1871-4080","1871-4099"],"issn-type":[{"value":"1871-4080","type":"print"},{"value":"1871-4099","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,19]]},"assertion":[{"value":"1 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 November 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 February 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 February 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"54"}}