Artificial Neural Network with Dropout and Batch Normalization Applied on Diabetic Patient Data

In this article, the data set of diabetic patients that was used to test the Random Interaction Forest (RIF) algorithm is examined. An Artificial Neural Network (ANN) is designed which matches the accuracy obtained by the RIF algorithm earlier. The proposed ANN model has several regularization methods which are added to prevent over-fitting. The benefits of combining dropout layers with batch normalization layers are justified using the Central Limit Theorem. The model is tuned with TensorFlow’s Bayesian Optimization Tuner to explore a more extensive range of hyper-parameters at a faster pace.