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.