ABSTRACT
The treatment of diseases is performed on the basis of symptoms by doctors. These symptoms along with the methods of treatment are digitized in databases locally and in a distributed manner. Medical datasets are growing day by day and are getting complicated. It is difficult to analyse these large multidimensional datasets manually. This multidimensional and huge data in terms of volume, velocity and variety needs an automated treatment for processing. We need to introduce different learning methods for treatment, prediction and diagnosis of diseases. The vast information of patient records makes it possible to treat diseases based on the symptoms using various analytical algorithms. In this chapter, we will discuss descriptive analysis for finding statistical associations of symptoms and hidden information within the datasets for better treatment. We will provide detailed methods for predictive analysis that can be used to diagnose a disease based on various disease symptoms and past expert treatments. Additionally, we will discuss prescriptive analysis that will provide a deep insight into very high-dimensional disease data for better management and decision-making. The analytics will not replace human interference and intervention but will provide a support system for proper and in-time decisions for better results.
