A comparison of soft computing models for Parkinson’s disease diagnosis using voice and gait features

Abstract

Parkinson’s disease is a widespread disease among elder population worldwide caused by dopamine loss, which reduces quality of life because of motor and non-motor complications. In the current paper, nine soft computing models, i.e., Cubist, Cubist Committees, Random Forests, Kernel Support Vector Machine, Linear Regression, Naive Bayes, Artificial Neural Network, Adaptive Neuro-Fuzzy Inference System, and Hybrid Neuro-Fuzzy Inference System are implemented for Parkinson’s disease diagnosis using voice and gait features. Later, their performances are evaluated based on performance measures, viz., true positive, false positive, false negative, true negative, accuracy, sensitivity, specificity, and RMSE, and finally, a comparison is performed to identify the most efficient model and data set combination. The comparison demonstrated that Random Forest model outperformed others yielding 100% accuracy, 100% sensitivity, 100% specificity, and zero RMSE on voice and gait training data sets both; Cubist Committees model outperformed others yielding 74.00% accuracy, 69.39% sensitivity, 78.43% specificity, and 0.4582 RMSE on voice testing data set; Random Forest model once again outperformed others yielding 81.66% accuracy, 92.39% sensitivity, 66.67% specificity, and 0.4283 RMSE on gait testing data set. Furthermore, these models’ performances are also evaluated on reduced feature vector voice and gait data sets obtained by Principal Component Analysis, and compared with their performances on former data sets. The comparison exhibited that the soft computing models’ performance decreases by reducing feature vector of the data sets.