Performance Analysis of Support Vector Machine (SVM) Model Through Parameter Optimization with Genetic Algorithm (GA) in Chronic Kidney Disease Classification

This study uses hyperparameter optimization to improve accuracy in classifying CKD or chronic kidney disease with the use of a Support Vector Machine algorithm or SVM. SVM is combined with the Grid Search technique for hyperparameter optimization, and SVM is combined with GA or Genetic Algorithm for a more adaptive optimization method. The test results show that combining SVM with Grid Search produces an average accuracy of 80.00%. However, when SVM is optimized using the Genetic Algorithm, the classification accuracy increases significantly to an average of 98.75%. The model optimized with the Genetic Algorithm performs significantly in both target classes. For class 0 (non-CKD), this model achieves a precision of 98.00%, a recall of 100%, and an F1-score of 99.00%. Meanwhile, for class 1 (CKD), the precision is recorded at 100%, a recall of 96.00%, and an F1-score of 98.00%. Genetic Algorithm for SVM hyperparameter optimization substantially improves the classification ability in detecting CKD, providing more precise results than the SVM-Grid Search method. The combination approach of SVM and Genetic Algorithm offers a more effective solution in classifying chronic kidney disease, which can improve the accuracy of medical diagnosis and help in better clinical decision-making.