A Hybrid Extreme Learning Machine Approach for Early Diagnosis of Parkinson's Disease

In this paper, we explore the potential of kernelized extreme learning machine (KELM) for efficient diagnosis of Parkinson's disease (PD). In the proposed method, the key parameters in KELM are investigated in detail. With the obtained optimal parameters, KELM manages to train the optimal predictive models for PD diagnosis. In order to further improve the performance of KELM models, feature selection techniques are implemented prior to the construction of the classification models. The effectiveness of the proposed method has been rigorously evaluated against the PD data set in terms of classification accuracy, sensitivity, specificity and the area under the ROC (receiver operating characteristic) curve (AUC).