Internet of things-inspired healthcare system for urine-based diabetes prediction

Abstract

Healthcare industry is the leading domain that has been revolutionized by the incorporation of Internet of Things (IoT) technology resulting in smart medical applications. Conspicuously, this study presents an effective system of home-centric Urine-based Diabetes (UbD) monitoring system. Specifically, the proposed system comprises of 4-layers for predicting and monitoring diabetes-oriented urine infection. The system layers including Diabetic Data Acquisition (DDA) layer, Diabetic Data Classification (DDC) layer, Diabetic-Mining and Extraction (DME) layer, and Diabetic Prediction and Decision Making (DPDM) layer allow an individual not exclusively to track his/her diabetes measure on regular basis but the prediction procedure is also accomplished so that prudent steps can be taken at early stages. Additionally, probabilistic measurement of UbD monitoring in terms of Level of Diabetic Infection (LoDI), which is cumulatively quantified as Diabetes Infection Measure (DIM) has been performed for predictive purposes using Recurrent Neural Network (RNN). Moreover, the existence of UbD is visualized based on the Self-Organized Mapping (SOM) procedure. To validate the proposed system, numerous experimental simulations were performed on datasets of 4 individuals. Based on the experimental simulation, enhanced results in terms of temporal delay, classification efficiency, prediction efficiency, reliability and stability were registered for the proposed system in comparison to state-of-the-art decision-making techniques.

Keywords: Diabetic monitoring system; Internet of things; Recurrent Neural Network (RNN); SOM visualization.