Hybrid Binary Grey Wolf Optimizer and Binary Sine Cosine Algorithms for Feature Selection Parkinson Disease - Nugroho, Andi; Warnars, Harco Leslie Hendric Spits; Isa, Sani Muhamad; Budiharto, Widodo (International Journal of Computing, 2026)
The algorithm used in the feature selection process is Hybrid Binary Grey Wolf Optimizer-Binary Sine Cosine Algorithm (HBGWO-BSCA). The dataset used for the HBGWO-BSCA is Parkinson’s. This dataset is used because Parkinson’s disease is one of the most frequently discussed diseases throughout the world. The aim of this paper is to find out what features are used in the process of predicting Parkinson’s disease. The HBGWO-BSC feature selection algorithm was proven to be able to increase the accuracy value of the KNN classification algorithm by 92%, while the HGWO-SCA only obtains an accuracy of 88%. The value of the HBGWO-BSCA is higher than that of HGWO-SCA because the relationship between features selected with the HBGWO BSCA is more accurate than that of the Hybrid Grey Wolf Optimizer-Sine Cosine Algorithm (HGWO-SCA). This proves that the HBGWO-BSC feature selection algorithm obtains the highest accuracy, precision, recall and F1-Score values compared to the HGWO-SCA. The HBGWO-BSCA in feature selection uses parameters N and alpha with parameter value ranges N= 9-11 and alpha=2-8. HBGWO-BSCA is an algorithm used for the feature selection process in datasets that already have labels or classes.