Today, diabetes is one of the most common, chronic, and, due to some complications, deadliest diseases in the world. The early detection of diabetes is very important for its timely treatment since it can stop the progression of the disease. The proposed method can help not only to predict the occurrence of diabetes in the future but also to determine the type of the disease that a person experiences. Considering that type 1 diabetes and type 2 diabetes have many differences in their treatment methods, this method will help to provide the right treatment for the patient. By transforming the task into a classification problem, our model is mainly built using the hidden layers of a deep neural network and uses dropout regularization to prevent overfitting. We tuned a number of parameters and used the binary cross-entropy loss function, which obtained a deep neural network prediction model with high accuracy. The experimental results show the effectiveness and adequacy of the proposed DLPD (Deep Learning for Predicting Diabetes) model. The best training accuracy of the diabetes type data set is 94.02174%, and the training accuracy of the Pima Indians diabetes data set is 99.4112%. Extensive experiments have been conducted on the Pima Indians diabetes and diabetic type datasets. The experimental results show the improvements of our proposed model over the state-of-the-art methods.

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Humanity continues to suffer from deadly diseases. Successes of science are great, but diseases that cannot be treated still exist. The only solution is to continue research in the search for drugs, as well as in methods of treating and preventing the onset of these diseases. This article is an overview of the development of AI on the issue of diabetes in a larger population of the planet over the past couple of years. The article contains information about the latest existing innovations of medical AI that helps in the fight against type 1 and type 2 of diabetes in 2019. A general assessment of existing AI systems and research has been conducted, as well as statistics on the distribution and usage of these technologies in the world today. The main problems that have not yet found a solution in the field of diagnosing and treating diabetes of both types are presented in conclusion.