Early diagnosis of Parkinson’s disease from multiple voice recordings by simultaneous sample and feature selection

• Published in: Expert systems with applications Vol. 137; pp. 22 - 28
• Main Authors: Ali, Liaqat, Zhu, Ce, Zhou, Mingyi, Liu, Yipeng
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 15.12.2019; Elsevier BV
• Subjects: Deep neural network; Feature selection; Hyper-parameters optimization; Parkinson's disease; Sample selection; Statistical methods; Statistical models; Statistical tests; Voice; Parkinson’S disease; Deep neural network; Feature selection; Sample selection; Hyper-parameters optimization
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2019.06.052

•We validate the hypothesis that multiple samples per subject might degrade the accuracy.•To enhance the accuracy, two dimensional data selection approach has been proposed.•The proposed method selects samples and features simultaneously, and outperforms the existing methods. Parkinson’s disease (PD) is a serious neurodegenerative disorder. It is reported that more than 90% of PD patients have voice impairments. Multiple types of voice recordings have been used for PD detection. Previous work indicates that the use of multiple types of samples per subject degenerates PD detection accuracy. In this paper, we validate it, and propose a two dimensional data selection method for sample and feature selection. The proposed method ranks features by using chi-square statistical model, searches optimal subset of the ranked features and iteratively selects samples. Experimental results show that the proposed method outperforms the state-of-the-art methods in terms of PD detection accuracy on multiple types of voice data.