Feature Selection Based on Artificial Bee Colony for Parkinson Disease Diagnosis

Parkinson's disease can be diagnosed by the speech signals. In general, the data obtained by feature extraction algorithms from the speech signals are used in any classification algorithm. Some of the extracted features have a high ability to represent the relevant problem, while others are low. In the diagnosis of Parkinson's disease, it is very important to determine which of the extracted features from the speech signals may increase the classification performance. In this paper, Artificial Bee Colony algorithm based feature selection approach is proposed for the solution of the mentioned problem. The proposed method has been analyzed in comparison with the well-known classification methods including support vector machine, k nearest neighbor, Naive Bayesian, decision tree.