A robust deep neural network framework for the detection of diabetes

Abstract

Significant developments occurred in numerous industries and fields during the digital age (1997–2006). One industry that has seen similar changes is the healthcare sector. Big data has primarily come from the healthcare sector since the 1990s. The outcomes of data analysis and dissemination have boosted healthcare and awareness. Perspectives and insights are the outcomes. The most important public health issue has been identified as diabetes and its repercussions. Based on patient medical imaging and records gathered, various techniques have been used to forecast diabetic complications. The technology of data mining has not been applied with much effort. This technique requires unstructured medical records, data entry, and output. Numerous methods have been employed to foresee diabetes problems. This study employs a deep learning technique to create a healthcare system to categorize and forecast the development of diabetes mellitus (Type 2). The Deep Belief Network, which includes the data collecting, pre-training, and classification processes of forecasting diabetes, is used to predict the complications of diabetes mellitus. The diabetic data set was subjected to the proposed DBN approach, which had an accuracy of 81.25%. Compared to other machine learning techniques, the suggested method produces results with higher accuracy.

Keywords

• Deep belief network
• Big data
• Deep learning
• Complication of diabetes