Parkinson's disease diagnosis by voice data using particle swarm optimization-extreme learning machine approach

Various speech processing approaches (e.g., acoustic feature extraction techniques) and Machine Learning (ML) algorithms have been applied to diagnosing Parkinson's disease (PD). However, the majority of these researches have used conventional techniques which obtain a low accuracy rate in diagnosing PD and still need further improvement. Particle Swarm Optimization-Extreme Learning Machine (PSO-ELM), one of the most recent and effective ML techniques, could be considered an accurate strategy in the classification process but has not been applied to solve the problem of PD diagnosis. Thus, in order to enhance the precision of the PD diagnosing, this study employs the PSO-ELM classifier and examines how well it performs on seven feature extraction techniques (basic features, WT (Wavelet Transform), MFCC (Mel Frequency Cepstral Coefficients), bandwidth + formant, intensity parameters, TQWT (Tunable Q-factor Wavelet Transform), and vocal fold features). The PSO-ELM approach has the capability to a) prevents overfitting, b) solve the binary and multi class classification issues, and c) perform like a kernel-based support vector machine with a structure of neural network. Therefore, if the combination of PSO-ELM classifier and appropriate feature extraction technique can improve learning performance, this combination can produce an effective method for identifying PD. In this study, the PD's voice samples have been taken from the Parkinson’s Disease Classification Benchmark Dataset. To discover a useful feature extraction technique to couple with the PSO-ELM classifier, we applied PSO-ELM to each extracted feature with the utilisation of unbalanced and balanced dataset. According to the experimental results, the MFCC features assist the PSO-ELM classifier to attaining its greatest accuracy, up to 97.35% using unbalanced dataset and 100.00% using balanced dataset. This shows that combining PSO-ELM with MFCC can improve learning performance, ultimately creating an effective method for identifying PD.