Review on Diagnosis of Diabetes Mellitus Using Different Approaches

Diabetes Mellitus is a metabolic disease commonly known as diabetes that caused due to chronic high blood sugar. In 2019, among the world population, 9.3 percent of adults have been suffered from diabetes. By the year 2045, the expected rise would be almost 11 percent of the world population. If diabetes Mellitus has not been predicted, it could damage nerves, eyes, kidneys, and other organs. So Early Prediction and treatment might save many lives. This paper deals with different models and approaches involved in the early prediction of diabetes. Even though there are various approaches available for predicting diabetes, this review paper visualizes the detailed analysis of Neural Network (NN), Machine Learning (ML), and Deep Learning (DL) approaches. By reviewing various approaches, it has proven that the deep learning models produce the accurate prediction in diabetes while compared with other models. The DL model approach for the prediction of diabetes is called DLPD (Deep Learning Prediction Diabetes). PIMA Indians Diabetes database from UCI machine learning is a commonly used dataset for diabetic prediction.