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A Deep Learning Approach For Detecting Type 2 Diabetes Mellitus

Diabetes is a disease that develops when blood glucose levels get too high. It can lead to a slew of serious complications because it is linked to many other health concerns. According to rising morbidity in recent years, the world's diabetic patients will number 700 million by 2045, implying that one out of every ten adults will be diabetic. There is no doubt that this remarkable figure requires immediate attention. Diabetes must be detected early in order to receive timely treatment and to prevent the disease from progressing. Because of the rapid improvement in technology, the deep learning approach was chosen for this project. Deep Learning with its extraordinary advancement has been able to provide its horizons in every field including the medical field. This application is built by importing several libraries and the Tkinter module for Graphical user interface (GUI). The PIMA Indian diabetes dataset is chosen from the UCI archive. In this proposed system, we extensively explored the most popular techniques in Deep Learning that are used to identify Type 2 Diabetes Mellitus. The model accomplished 84% accuracy with 80–20 train-test split.