Deep learning based big medical data analytic model for diabetes complication prediction

The revolution in digitization makes the health care sector as a prime source of big data. The analysis of these data could be a great supporting source for deriving new insights, which increases the care and awareness about health. Diabetes together with its complications has been recognized worldwide as a chief public health threat. Predicting diabetic complications is considered as a highly effectual technique for augmenting the survival rate of diabetic patients. While many studies currently use medical images and structured medical records, very limited efforts have been dedicated for applying Data Mining (DM) techniques for unstructured textual medical records, for instance, admission and discharge records. Many DM techniques have been generated for envisaging diabetic complications. But in existing methods, the classification as well as prediction accuracy is not so high. So this paper proposes a model centered on Deep Learning (DL) for predicting complications of Type 2 Diabetes Mellitus. The proposed model follows data collection, pre-training, feature extraction, Deep Belief Network (DBN), validation process, and classification steps for predicting diabetic complications. Finally, the performances proffered by the proposed DL based Big Medical Data Analytics model using DBN as well as the prevailing techniques are contrasted with respect to Precision, accuracy, and Recall. The Training, as well as the Testing process, delineates the pervasiveness of risk with an accuracy of 81.20%. This realistic prediction model will be very much useful for effectively managing diabetes.