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

Zhennao Cai,Caiyun Wen,Huiling Chen,Dong Zhao,Chunyu Huang,Hui Huang,Jun Li,Jianhua Gu,Changfei Tong

doi:10.1155/2018/2396952

Abstract

Parkinson's disease (PD) is a common neurodegenerative disease, which has attracted more and more attention. Many artificial 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. The proposed method, an evolutionary instance-based learning approach termed CBFO-FKNN, was developed by coupling the chaotic bacterial foraging optimization with Gauss mutation (CBFO) approach with FKNN. The integration of the CBFO technique efficiently resolved the parameter tuning issues of the FKNN. The effectiveness of the proposed CBFO-FKNN was rigorously compared to those of the PD datasets in terms of classification accuracy, sensitivity, specificity, and AUC (area under the receiver operating characteristic curve). The simulation results indicated the proposed approach outperformed the other five FKNN models based on BFO, particle swarm optimization, Genetic algorithms, fruit fly optimization, and firefly 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. The 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.

Highlights

Parkinson's disease (PD), a degenerative disorder of the central nervous system, is the second most common neurodegenerative disease [1]
In order to verify the validity of the proposed algorithm, the original bacterial foraging optimization (BFO), Firefly Algorithm(FA)[76], Flower Pollination Algorithm (FPA)[77], Bat Algorithm (BA)[78], Dragonfly Algorithm (DA)[79], Particle Swarm Optimization (PSO)[80], and the improved BFO called PSOBFO were compared on these issues
The results demonstrate that the utilized chaotic mapping strategy and Gaussian mutation in the chaotic bacterial foraging optimization with Gauss mutation (CBFO) technique have improved the efficacy of the classical BFO, in a significant manner

Summary

Introduction

Parkinson's disease (PD), a degenerative disorder of the central nervous system, is the second most common neurodegenerative disease [1]. Research has shown phonation and speech disorders are common among PD patients [9]. Phonation and speech disorders can appear in PD patients as many as five years before being clinically diagnosed with the illness [10]. In FKNN, the fuzzy membership values of samples are assigned to different categories as follows:. Uij denotes the degree of membership of the pattern xj from the training set to class i among the k-nearest neighbors of x. The constrained fuzzy membership approach was adopted in that the k-nearest neighbors of each training pattern (i.e., xk) were determined, and the membership of xk in each class was assigned as uij (xk)

Methods

Results

Conclusion