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. 2021 Nov 19;23(11):e29554. doi: 10.2196/29554

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©Xinyao Hou, Yu Zhang, Yanping Wang, Xinyi Wang, Jiahao Zhao, Xiaobo Zhu, Jianbo Su. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 19.11.2021.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

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Receiver operating characteristics (ROC) analysis for the diagnosis of Parkinson disease using texture features and the recognition result. (A) ROC curve for 2 texture feature extraction algorithms with 3 classification models and (B) best result of machine learning algorithms. (C) ROC curve for texture features of the region of the eye, region of the mouth and their combination and (D) result of machine learning algorithms. AUC: area under the curve; FPR: false positive rate; HOG: histogram of oriented gradient; KNN: k-nearest neighbor; LBP: local binary patern; SVM: support vector machine; TPR: true positive rate.