{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T16:12:17Z","timestamp":1781280737784,"version":"3.54.1"},"reference-count":44,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,7,9]],"date-time":"2022-07-09T00:00:00Z","timestamp":1657324800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012226","name":"the Fundamental Research Funds for the Central Universities of China","doi-asserted-by":"publisher","award":["20720200033"],"award-info":[{"award-number":["20720200033"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"the Fundamental Research Funds for the Central Universities of China","doi-asserted-by":"publisher","award":["2021R0001"],"award-info":[{"award-number":["2021R0001"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"the Fundamental Research Funds for the Central Universities of China","doi-asserted-by":"publisher","award":["2021-3502-04-002336"],"award-info":[{"award-number":["2021-3502-04-002336"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"the Fundamental Research Funds for the Central Universities of China","doi-asserted-by":"publisher","award":["2021-3502-04-005565"],"award-info":[{"award-number":["2021-3502-04-005565"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Fujian Provincial Innovation Strategy Research Project","award":["20720200033"],"award-info":[{"award-number":["20720200033"]}]},{"name":"Fujian Provincial Innovation Strategy Research Project","award":["2021R0001"],"award-info":[{"award-number":["2021R0001"]}]},{"name":"Fujian Provincial Innovation Strategy Research Project","award":["2021-3502-04-002336"],"award-info":[{"award-number":["2021-3502-04-002336"]}]},{"name":"Fujian Provincial Innovation Strategy Research Project","award":["2021-3502-04-005565"],"award-info":[{"award-number":["2021-3502-04-005565"]}]},{"name":"Enterprise-Funded Crosswise Projects","award":["20720200033"],"award-info":[{"award-number":["20720200033"]}]},{"name":"Enterprise-Funded Crosswise Projects","award":["2021R0001"],"award-info":[{"award-number":["2021R0001"]}]},{"name":"Enterprise-Funded Crosswise Projects","award":["2021-3502-04-002336"],"award-info":[{"award-number":["2021-3502-04-002336"]}]},{"name":"Enterprise-Funded Crosswise Projects","award":["2021-3502-04-005565"],"award-info":[{"award-number":["2021-3502-04-005565"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biosensors"],"abstract":"<jats:p>This article proposes a novel semi-supervised competitive learning (SSCL) algorithm for vocal pattern classifications in Parkinson\u2019s disease (PD). The acoustic parameters of voice records were grouped into the families of jitter, shimmer, harmonic-to-noise, frequency, and nonlinear measures, respectively. The linear correlations were computed within each acoustic parameter family. According to the correlation matrix results, the jitter, shimmer, and harmonic-to-noise parameters presented as highly correlated in terms of Pearson\u2019s correlation coefficients. Then, the principal component analysis (PCA) technique was implemented to eliminate the redundant dimensions of the acoustic parameters for each family. The Mann\u2013Whitney\u2013Wilcoxon hypothesis test was used to evaluate the significant difference of the PCA-projected features between the healthy subjects and PD patients. Eight dominant PCA-projected features were selected based on the eigenvalue threshold criterion and the statistical significance level (p &lt; 0.05) of the hypothesis test. The SSCL algorithm proposed in this paper included the procedures of the competitive prototype seed selection, K-means optimization, and the nearest neighbor classifications. The pattern classification experimental results showed that the proposed SSCL method can provide the excellent diagnostic performances in terms of accuracy (0.838), recall (0.825), specificity (0.85), precision (0.846), F-score (0.835), Matthews correlation coefficient (0.675), area under the receiver operating characteristic curve (0.939), and Kappa coefficient (0.675), which were consistently better than those results of conventional KNN or SVM classifiers.<\/jats:p>","DOI":"10.3390\/bios12070502","type":"journal-article","created":{"date-parts":[[2022,7,10]],"date-time":"2022-07-10T21:19:28Z","timestamp":1657487968000},"page":"502","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Classification of Dysphonic Voices in Parkinson\u2019s Disease with Semi-Supervised Competitive Learning Algorithm"],"prefix":"10.3390","volume":"12","author":[{"given":"Guidong","family":"Bao","sequence":"first","affiliation":[{"name":"School of Informatics, Xiamen University, 422 Si Ming South Road, Xiamen 361005, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengchen","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Informatics, Xiamen University, 422 Si Ming South Road, Xiamen 361005, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoqian","family":"Sang","sequence":"additional","affiliation":[{"name":"School of Informatics, Xiamen University, 422 Si Ming South Road, Xiamen 361005, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yangcan","family":"Hou","sequence":"additional","affiliation":[{"name":"School of Informatics, Xiamen University, 422 Si Ming South Road, Xiamen 361005, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yixuan","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Informatics, Xiamen University, 422 Si Ming South Road, Xiamen 361005, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3612-7818","authenticated-orcid":false,"given":"Yunfeng","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Informatics, Xiamen University, 422 Si Ming South Road, Xiamen 361005, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1783","DOI":"10.1016\/S0140-6736(04)16305-8","article-title":"Parkinson\u2019s disease","volume":"353","author":"Samii","year":"2004","journal-title":"Lancet"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Yang, S., Zheng, F., Luo, X., Cai, S., Wu, Y., Liu, K., Wu, M., Chen, J., and Krishnan, S. 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