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

Haolun Li; Rui Zong; Xin Xu; Long Sheng Pan; Qionghai Dai; Feng Xu; Hao Gao; Wensheng Wang

doi:10.1117/12.2549515

16 March 2020 Diagnosis of Parkinson's Disease with a hybrid feature selection algorithm based on a discrete artificial bee colony

Haolun Li, Rui Zong, Xin Xu, Long Sheng Pan, Qionghai Dai, Feng Xu, Hao Gao, Wensheng Wang

Author Affiliations +

Proceedings Volume 11314, Medical Imaging 2020: Computer-Aided Diagnosis; 113142S (2020) https://doi.org/10.1117/12.2549515
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

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.

Citation Download Citation

Haolun Li, Rui Zong, Xin Xu, Long Sheng Pan, Qionghai Dai, Feng Xu, Hao Gao, and Wensheng Wang "Diagnosis of Parkinson's Disease with a hybrid feature selection algorithm based on a discrete artificial bee colony", Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113142S (16 March 2020); https://doi.org/10.1117/12.2549515

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