Earlier prediction of Parkinson's disease using cross non-decimated wavelet transform and machine learning algorithm

Parkinson's disease (PD) is a brain disorder, that affects a person's body movement causing stiffness, shaking and imbalance. Earlier detection of PD is a challenging task for researchers. In this paper, earlier detection of PD is performed using the Cross-Non-Decimated Wavelet Transform (CNDWT) and Bayesian Optimized Multiple Linear Regression (BOMLR) algorithm. The PD voice data is amplitude-sliced, augmented and processed with CNDWT. The CNDWT decomposes amplitude-sliced augmented PD voice data with the Haar transform and reconstruction is performed using Daubechies wavelet of order 3 (DB3) transformations. Preprocessed data is correlated for identification of highly influential data attributes of PD. The Bayesian Optimized Multiple Linear Regression (BOMLR) method is applied to the highly correlated data attributes for earlier PD prediction. A voice data set is created in this study, which consists of 31 voice recordings of which 23 are from individuals affected by PD. The proposed CNDWT method is compared with existing methods. The results show that the proposed CNDWT method outperforms other traditional algorithms with an accuracy of 99% in predicting Parkinson's disease.