Chapter 9 - Intelligence-Based Health Recommendation System Using Big Data Analytics

Abstract

In today's digital world, healthcare is one of the core areas in the medical domain. A healthcare system is required to analyze a large amount of patient data, which helps to derive insights and predictions of disease. This system should be intelligent and able to predict the patient's health condition by analyzing the patient's lifestyle, physical health records, and social activities. The health recommendation system (HRS) is becoming an important platform for healthcare services. In this context, health intelligent systems have become indispensable tools in decision-making processes in the healthcare sector. The main objective is to ensure the availability of valuable information at the right time by ensuring information quality, trustworthiness, authentication, and privacy. As people use social networks to learn about their health condition, so the HRS is very important to derive outcomes such as recommending diagnosis, health insurance, clinical pathway-based treatment methods, and alternative medicines based on the patient's health profile. In this chapter, we discuss recent research that targeted utilization of large volumes of medical data while combining multimodal data from disparate sources, which reduces the workload and cost in healthcare. In the healthcare sector, big data analytics using a recommendation system has an important role in terms of decision-making processes regarding the patient's health. This chapter presents a proposed intelligent HRS that provides an insight into how to use big data analytics for implementing an effective health recommendation engine and shows how to transform the healthcare industry from the traditional scenario to more personalized paradigm in a tele-health environment. Our proposed intelligent HRS resulted in lower MAE value when compared to existing approaches.