Data and knowledge discovery are essential in the healthcare industry for disease diagnosis and prediction. Patient records and disease-related information are data in the health industry. The DCKSVM and HRBFNN approaches are utilized to predict the result. The aim is for evaluating the efficiency of the DCKSVM-Divide and Conquer Kernal Support Vector Machine learning and RBFNN-Radial Basis Function Neural Network algorithm in detecting diabetes in the general population. Diabetic prediction can be predicted by many existing algorithms, but machine learning algorithms for used this research. In this research, the KNN algorithm is used as a preprocessing tool to fulfill the missing attribute value in the dataset. The HRBFNN approach gives good accuracy. The result shows that the RBFNN algorithm give good accuracy than SVM in predicting diabetics.
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