Binary Grey Wolf Optimizer in Diabetes Prediction
Keywords:
BGWO, Classification, Diabetes, KNN, SVMAbstract
Diabetes is a disease that is feared today because it has claimed quite a lot of victims. Thus knowing diabetes early can prevent death from diabetes. However, for diabetes detection classification tools, there is still no one that uses z-score normalization in the classification of the PIMA diabetes dataset, The purpose of this research is to be able to find a classification model for predicting diabetes from an early age. we will take a public dataset from the Pima Indians Diabetes Database (PIDD). The method that will be used in this study uses an optimization approach with the Improve Binary Grey Wolf Optimizer (BGWO) and Z-Score normalization, for testing it will use classification algorithms such as Support Vector Machine (SVM), Decision Tree (DT), k-Nearest Neighbors (KNN). The BGWO optimization algorithm and Z-Score normalization is used to optimize the performance of the SVM, DT, and KNN classification algorithm in producing the best accuracy value. The BGWO optimization algorithm and Z-Score normalization can increase the accuracy value from KNN = 76.5%, DT = 74.7%, and SVM = 77.7% to KNN + BGWO = 78%, DT + BGWO = 76.2% and SVM + BGWO = 79.8% in PIDD dataset.
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