A novel approach for smart-healthcare recommender system

Abstract

With the expeditious development of big data and internet of things (IoT), technology has successfully associated with our everyday life activities with smart healthcare being one. The global acceptance toward smart watches, wearable devices, or wearable biosensors has paved the way for the evolution of novel applications for personalized e-Health and m-Health technologies. The data gathered by wearables can further be analyzed using machine learning algorithms and shared with medical professionals to provide suitable recommendations. In this work, we have analyzed the performance of different machine learning techniques on public datasets of healthcare to select the most suitable one for the proposed work. Based on the results, it is observed that random forest model performs the best. Further, we propose a quantified self-based hybrid model for smart-healthcare environment that would consider user health from multiple perspectives and recommend suitable actions.