Diabetes is one of the chronic diseases rovering all over the world. It affects people in all ages. Even child by birth also getting affected by this disease. Already various machine learning algorithms were used to predict diabetes. This work compares two algorithms Enhanced Catboost with Linear Discriminant Analysis (ECB-LDA) and Dolphin Swarm Optimization with Radial Basis Neural Network (DSO-RBNN) which were used for diabetes prediction. Also hospitals and other clinical centers are facing problem in handling large amount of data. To solve such problem and also do early prediction of diabetes, big data analytics is used. This work proves that the accuracy of DSO-RBNN is better than the ECB-LDA.