An adaptive kernel-based weighted extreme learning machine approach for effective detection of Parkinson's disease

Imbalanced data appear in many real-world applications, from biomedical application to network intrusion or fraud detection, etc. Existing methods for Parkinson's disease (PD) diagnosis are usually more concerned with overall accuracy (ACC), but ignore the classification performance of the minority class. To alleviate the bias against performance caused by imbalanced data, in this paper, an effective method named AABC-KWELM has been proposed for PD detection. First, based on a fast classifier extreme learning machine (ELM), weighted strategy is used for dealing with imbalanced data and non-linear mapping of kernel function is used for improving the extent of linear separation. Furthermore, both binary version and continuous version of an adaptive artificial bee colony (AABC) algorithm are used for performing feature selection and parameters optimization, respectively. Finally, PD data set is used for evaluating rigorously the effectiveness of the proposed method in accordance with specificity, sensitivity, ACC, G-mean and F-measure. Experimental results demonstrate that the proposed AABC-KWELM remarkably outperforms other approaches in the literature and obtains better classification performance via 5-fold cross-validation (CV), with specificity of 100%, sensitivity of 98.62%, ACC of 98.97%, G-mean of 99.30%, and F-measure of 99.30%.