Smart Healthcare Framework for Asthma Attack Prediction and Prevention

Abstract

Smart healthcare is one of the most exciting applications introduced to provide better disease diagnosis and prediction tools. Recent studies introduced various disease prediction models that employ machine learning techniques by utilizing different features that cause the disease. Asthma exacerbates due to various triggers, including personal and environmental variables. A few works had introduced a complete smart healthcare framework that combines the prediction model with a trigger visualization system. These studies generally use a limited number of triggers in the prediction and visualization process, and traditional machine learning techniques. This paper proposes a smart healthcare framework that provides patients with a sophisticated tool to visualize the asthma trigger and notify them about any predictable attack. The asthma attack prediction is based on utilizing a comprehensive set of patient data and different environmental triggers. It is based on deploying deep learning instead of the traditional machine learning techniques. The prevention model includes a dynamic visualization map of air pollution concentration, which alerts users with high-risk areas. Moreover, a context-aware safe-route recommendation system is proposed to keep users away from any asthma triggers.