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Clinical tests have long been considered appropriate in diagnosing chronic kidney disease (CKD) because of their noninvasiveness, simplicity, and cost. Timely detection and management of CKD are the most effective methods to address the expanding global burden induced by CKD. We adopted an S-MTL (Supervised Multi-task Learning) approach and combined SimpleRNN (Simple Recurrent Neural Network) and MLP (Multi-Layer Perception) to develop a hybrid model-CKD.Net to predict five CKD stages. This hybrid neural network architecture was trained on massive clinical datasets with heterogeneous 27 features to predict kidney function. We employed various data augmentation strategies to balance the five CKD stage datasets and meticulously utilized the hyperparameter to minimize the loss and validation loss to reduce overfitting and hence increase model generalization. Performance comparisons of …