Diagnosis of Parkinson's Disease with a hybrid feature selection algorithm based on a discrete artificial bee colony

Parkinson's disease (PD) is a central nervous system disease that common occurs among older peoples. Automatic identification the degree of early Parkinson's disease is a meaningful work today. In this paper, the diagnostic experiment on Parkinson is executed on feature data sets which contain a variety data of handwriting and speech gathered from Parkinson's patients and ordinary people. Since most data in these datasets contains noise, right selection of feature from subsets plays a significant role on enhancing the performance of classification. To increase the accuracy of classification, this paper proposes an improved discrete artificial bee colony algorithm to determine the optimal subset of features. Our algorithm combines both advantages of filters and wrappers feature selection strategies to eliminate most of the uncorrelated or noisy features which could improve the efficiency of feature selection. The experimental results demonstrate our algorithm achieves better results on both precision and shortening the amounts of subsets among the comparison with other modern Evolutionary Computation algorithms.