An IoT based big data framework using equidistant heuristic and duplex deep neural network for diabetic disease prediction

Abstract

A number of strategies empowered by Internet of Thing (IoT) have been utilized for the prediction of several dreadful diseases like diabetic for which ceaseless and real-time tracing system is an exceptionally predominant one. Wearable medical devices with sensor have consistently producing abundant data volume referred to as big data. With higher speed of data creation, it becomes cumbersome to accumulate, operate and analyze such abundant data volume during emergency. Deep Learning (DL) approaches have been largely made use of perceiving patterns, categorizing objects and the prediction of diabetic diseases at an early stage. Even so, DLs are fundamentally modest in evaluation precisely when the diabetic disease data size is abundant. To achieve the expectations of DLs for big data applications from an electronic device, the evaluation procedure for diabetic disease prediction must be speeded up, so that early analysis can be arrived at. In this work, a method called Equidistant Heuristic and Duplex Deep Neural Network (EH-DDNN) for early diabetic disease prediction is proposed. First, with the Big Data dataset as input, Equidistant Heuristic Pruning (EHP) algorithm is presented for feature selection. The EHP splits the input data matrix into rows and columns separately. By utilizing the notion of conditional non-alignment assessment and heuristics techniques, EHP, exploits neighbourhood evaluations into sub-division while reducing the communication time and overhead, thus enormously correlating computations. This in turn removes the irrelevant as well as redundant features, therefore resulting with fewer features easier for early prediction. Next, with the inherent features, a Duplex Deep Neural Network (DDNN) is designed for early prediction analyses a fusion of nonlinear processing features and linear response for stockpiling abundant data volume. Experiments are conducted and validation is performed on the benchmark datasets, Diabetes Data Set from UCI repository and Pima Indians Diabetes Disease dataset. Comparative analysis of diabetic disease prediction time, diabetic disease prediction overhead and ROC curve analysis are made.