CNN-enabled transfer learning and cosine rat swarm optimisation for classification of heart disease

This research introduces the cosine rat swarm optimisation (CRSO)-based transfer learning (TL) model for the classification of heart disease by using medical data. Originally, the input medical data are accumulated and then the image is pre-processed by using the min-max normalisation. Then, the feature fusion is done using Matusita similarity measures considering the deep maxout network (DMN). Thereafter, the borderline – synthetic minority over-sampling technique (SMOTE) oversampling model is used to augment the data. Then, the classification of heart disease is carried out by using a convolution neural network (CNN) with transfer learning wherein the CNN is used with the hyperparameters from the trained models like deep batch-normalised eLU AlexNet (DbneAlexnet). Here, the training of DbneAlexnet is done using the CRSO algorithm. The proposed method achieved an accuracy of 91.7%, with a true negative rate (TNR) of 91% and a true positive rate (TPR) of 91.8%.