As diagnosing diabetes is a challenging and tedious task for physicians, it has attracted researchers to focus on designing medical decision support systems with higher accuracy. In this paper, a clinical decision support system based on Extreme Learning Machine (ELM) and Simulated Annealing (SA) is proposed for the diagnosis of diabetes using the Pima Indian Diabetes (PID) dataset of the UCI machine learning repository. Classification is performed using ELM while optimization of ELM parameter is carried out by Simulated Annealing heuristics. The performance of the proposed system is analyzed based on the parameters such as classification accuracy, sensitivity and specificity using 10-fold cross-validation and confusion matrix. The accuracy of the proposed system is found to be superior to other existing systems in the literature. The system is validated for different datasets like Hepatitis, Breast Cancer and Cardiac Arrhythmia. © Research India Publications.