An Expert System based on SVM and Hybrid GA-SA Optimization for Disease Diagnosis

Abstract: An accurate diagnosis of diseases by physicians is a tedious and challenging task. This challenge can be addressed by designing and implementing medical expert systems with utmost accuracy. This paper proposes a medical expert system based on Support Vector Machine (SVM) and hybrid Genetic Algorithm (GA) –Simulated Annealing (SA) for the diagnosis of a set of diseases by using the dataset of UCI machine learning repository. The SVM with Gaussian Radial Basis Function (RBF) kernel performs the classification process. The hybrid GA-SA is used for the selection of the most significant feature subset of the dataset and for the optimization of the kernel parameters of SVM. The performance of the expert system is analyzed using various parameters like classification accuracy, sensitivity and specificity. The proposed system is validated using different disease dataset like Pima Indian Diabetes (PID), breast cancer, hepatitis and cardiac arrhythmia. The classification accuracy of the proposed system is found to be superior to that of the other existing systems in the literature.