Communicable diseases are transmitted through water, food, contaminated surfaces, bodily fluids, air. In such a situation, staying in home isolation for fewer chronic health problems and monitoring health status frequently through Medical Sensors (MSs) is recommended. The use of Artificial Intelligence (AI) in smart consumer electronics and sustainable healthcare has recently demonstrated remarkable results. However, the healthcare domain requires high levels of accountability and transparency for communicable disease prediction and sustainable life in edge networks. This paper aims to present an intelligent healthcare prototype that can identify risk factors according to monitoring parameters by analyzing the Explainable XGBoost (XXGB) model. Using edge networks for sustainable living, we explore the intersection between healthcare and consumer electronics. Initially, the prototype has been trained using the XXGB model over one publicly available dataset related to communicable diseases. Next, the prototype identifies patient risk factors by analyzing real-time monitoring parameters. Simulation results illustrate the efficiency of the proposed XXGB model up to 84.2% accuracy, which is higher than existing models.