Efficient feature selection with attention based deep cat convolutional stacked sparse autoencoder for diabetes prediction

Diabetes, one of the most serious diseases in the world, however, early detection can prevent diabetes. This work proposes a novel approach to identifying early signs of diabetes based on deep learning methods. First, the input data is pre-processed and the features are selected using an improved Cheetah Optimization (ICO). Finally, diabetes is classified using a dual attention-based deep cat convolutional stacked sparse autoencoder model (DA_DCC_SSAE). The proposed study improves the results and proves that the proposed method produces better results in terms of accuracy (98.4% - dataset-1, 98% - dataset-2, 97.4% - dataset-3, and 96.8% - dataset-4.