Binary Grey Wolf Optimizer with Mutation and Adaptive K-nearest Neighbour for Feature Selection in Parkinson's Disease Diagnosis

Disease identification and classification relies on Feature Selection (FS) to find the relevant features for accurate medical diagnosis. FS is an optimization problem solved with the help of stochastic optimization strategies. Research on selection of relevant features in Parkinson's disease is challenging due to the lack of enough information about the disease. In this work, a wrapper-based Binary Improved Grey Wolf Optimizer (BIGWO) approach is developed for categorizing the Parkinson's disease with optimal set of features. The proposed approach employs five different transfer functions to encode the search space of features. A mutation operation in BIGWO fine tunes the search process to determine the best features for disease diagnosis. Further, the number of neighbours in k-Nearest Neighbour (kNN) is optimized using the Adaptive kNN (AkNN) in the fitness evaluation of the BIGWO algorithm which improves classification performance. Different variants of the proposed BIGWO algorithm are compared with other algorithms to assess their performance. Experiments are conducted on four benchmark datasets that demonstrate the two variant BIGWO-V1 and BIGWO-V2 algorithms outperform other four meta-heuristic (GA, PSO, BBA, and MCS) algorithms. (C)2022 Elsevier B.V. All rights reserved.

Automated summary

This article introduces a wrapper-based Binary Improved Grey Wolf Optimizer (BIGWO) approach for categorizing Parkinson's disease and feature selection. The proposed method utilizes an adaptive k-nearest neighbor algorithm to optimize the feature selection process, and experimental results demonstrate its superior performance compared to other algorithms.