An Intelligent Parkinson’s Disease Diagnostic System Based on a Chaotic Bacterial Foraging Optimization Enhanced Fuzzy KNN Approach

Parkinson's disease (PD) is a common neurodegenerative disease, which has attracted more and more attention. Many artifcial intelligence methods have been used for the diagnosis of PD. In this study, an enhanced fuzzy k-nearest neighbor (FKNN) method for the early detection of PD based upon vocal measurements was developed. Te proposed method, an evolutionary instancebased learning approach termed CBFO-FKNN, was developed by coupling the chaotic bacterial foraging optimization with Gauss mutation (CBFO) approach with FKNN. Te integration of the CBFO technique efciently resolved the parameter tuning issues of the FKNN. Te efectiveness of the proposed CBFO-FKNN was rigorously compared to those of the PD datasets in terms of classifcation accuracy, sensitivity, specifcity, and AUC (area under the receiver operating characteristic curve). Te simulation results indicated the proposed approach outperformed the other fve FKNN models based on BFO, particle swarm optimization, Genetic algorithms, fruit fy optimization, and frefy algorithm, as well as three advanced machine learning methods including support vector machine (SVM), SVM with local learning-based feature selection, and kernel extreme learning machine in a 10-fold cross-validation scheme. Te method presented in this paper has a very good prospect, which will bring great convenience to the clinicians to make a better decision in the clinical diagnosis.