A Modified Maximum Relevance Minimum Redundancy Feature Selection Method Based on Tabu Search For Parkinson’s Disease Mining

Abstract

Parkinson’s disease is a complex chronic neurodegenerative disorder of the central nervous system. One of the common symptoms for the Parkinson’s disease subjects, is vocal performance degradation. Patients usually advised to follow personalized rehabilitative treatment sessions with speech experts. Recent research trends aim to investigate the potential of using sustained vowel phonations for replicating the speech experts’ assessments of Parkinson’s disease subjects’ voices. With the purpose of improving the accuracy and efficiency of Parkinson’s disease treatment, this article proposes a two-stage diagnosis model to evaluate an LSVT dataset. Firstly, we propose a modified minimum Redundancy-Maximum Relevance (mRMR) feature selection approach, based on Cuckoo Search and Tabu Search to reduce the features numbers. Secondly, we apply simple random sampling technique to dataset to increase the samples of the minority class. Promisingly, the developed approach obtained a classification Accuracy rate of 95% with 24 features by 10-fold CV method.