Abstract
Parkinson’s disease (PD) is a progressive and long-term neurodegenerative disorder of the central nervous system. It has been studied that 90% of the PD subjects have voice impairments which are some of the vital characteristics of PD patients and have been widely used for diagnostic purposes. However, the curse of dimensionality, high aliasing, redundancy, and small sample size in PD speech data bring great challenges to classify PD objects. Feature reduction can efficiently solve these issues. However, existing feature reduction algorithms ignore high aliasing, noise, and the stability of algorithms, and thus fail to give substantial classification accuracy. To mitigate these problems, this study proposes a weighted hybrid feature reduction embedded with ensemble learning technique which comprises (1) hybrid feature reduction technique that increases inter-class variance, reduces intra-class variance, preserves the neighborhood structure of data, and remove co-related features that causes high aliasing and noise in classification. (2) Weighted-boosting method to train the model precisely. (3) Furthermore, the stability of the algorithm is enhanced by introducing a bagging strategy. The experiments were performed on three different datasets including two widely used datasets and a dataset provided by Southwest Hospital (Army Military Medical University) Chongqing, China. The experimental results indicated that compared with existing feature reduction methods, the proposed algorithm always shows the highest accuracy, precision, recall, and G-mean for speech data of PD. Moreover, the proposed algorithm not only shows excellent performance for classification but also deals with imbalanced data precisely and achieved the highest AUC in most of the cases. In addition, compared with state-of-the-art algorithms, the proposed method shows improvement up to 4.53%. In the future, this algorithm can be used for early and differential diagnoses, which are rated as challenging tasks.
Highlights
The use of machine learning techniques to control diseases is becoming popular nowadays [1,2,3]
Calculate A (Affinity matrix) Solve for P (The projection matrix). In this sFecetaiotunr,ethSeelreecsutilotsnof the proposed algorithm are analyzed and compared with feature selection, featIunrietiatrlaiznesfworemigahttioWn,(afn)d=so0mfeosrtaeatec-hofo-fthteh-earfet aatlugroersithms which have been extensively usedCfaolrcuPDlatdeiadginfofsifs., xFo,rMv(axlid)ati(odnifpfeurrepnocseeos,f hsoamldp-olue t-xcroasns-dvanleidigahtiboonr is used in which the dataset is randomly saanmd peqleuoaflltyhdeisvaidmeedcilnatsos)training (1/3), validation (v1a/li3d)aatniodntpesrot c(1e/ss3)casneCtaes.flfcSeuiclntaictveeeeldyaicafhvfosufidb, xjdeac, tHtac(ooxnvte)arilna(psdpimfifnuegrlet.inpclee osafmsapmlepsleinxtheadndatanseeigt,htbhoisr
It is worth noticing that for PSDMTSR datasets, the proposed method always performed well compared with feature extraction methods
Summary
The use of machine learning techniques to control diseases is becoming popular nowadays [1,2,3]. Parkinson’s disease damages the nerve cells that are responsible for body movement [4]. The convenience of voice acquisition makes remote monitoring of Parkinson’s disease possible. Speech datasets often have noise and high aliasing characteristics. This brings troublesomeness in the processing of speech data. How to extract efficient representational features from Parkinson’s speech dataset has received much attention from researchers. Dimensionality reduction is a technique wherein some of the features are alleviated from the original high dimensional data space in such a way that new lower-dimensional data space can effectively represent the original data space. Dimensionality reduction procedures are mainly divided into feature selection and feature transformation [5]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
