Optimization of SVM Parameters with Hybrid CS-PSO Algorithms for Parkinson's Disease in LabVIEW Environment.

Duygu Kaya

doi:10.1155/2019/2513053

Abstract

Optimization is the process of achieving the best solution for a problem. LabVIEW based on an SVM model is proposed in this paper to get the best SVM parameters using the hybrid CS and PSO method. PCA is used as a preprocessor of SVM for reducing the dimension of data and extracting features of training samples. Also, SVM parameters are optimized for Parkinson's disease data by combining CS and PSO. The designed system is used to determine the best SVM parameters, and it is compared to PSO and CS optimization methods and found that the used CS-PSO hybrid optimization method is better. The hybrid model shows that the accuracy of the performance achieved is 97.4359%. Also, the data classification results obtained by using SVM parameters determined by optimization are measured by precision, recall, F1 score, false positive rate (FPR), false discovery rate (FDR), false negative rate (FNR), negative predictive value (NPV), and Matthews' correlation coefficient (MCC) parameters.

Highlights

Parkinson’s disease (PD) is a neurological disorder that affects the standard of life of the patients and their relatives
Accurate and reliable diagnosis is very important for human health
Dimension reduction and normalization procedures were performed to extract properties from the used data to ensure that they are in a single order. en, optimization methods are applied to support vector machine (SVM)

Summary

Introduction

Parkinson’s disease (PD) is a neurological disorder that affects the standard of life of the patients and their relatives. Support vector machine (SVM) is based on statistical learning theory and is the most effective algorithm for predicting performance for nonlinear problems. The cuckoo search algorithm (CS) is another optimization method It has less parameters, easy to implement, and efficient. Different optimization algorithms have been used to obtain the best SVM parameters for predicting Parkinson’s disease. Contribution (i) A different work environment for researchers has been proposed using LabVIEW, a visual programming language instead of only text-based programming languages (ii) Hybrid optimization methods are used for obtaining the best SVM parameters.

Description of Used Techniques

Results

The Proposed Methods

Discussion and Conclusion

Method