Health Data Analytics
DOI link for Health Data Analytics
Health Data Analytics book
This chapter discusses few data analytics methods relevant for healthcare data. It describes some data mining and machine learning models that have already been adapted to the healthcare domain. The chapter provides some statistical methods such as linear regression, and Bayesian models, advanced methods in machine learning and data mining, such as decision trees and artificial neural networks, and text mining methods. Databases, data mining, information retrieval techniques help in healthcare delivery as much as the outcomes of medical researchers and healthcare practitioners. The sources for medical data are very heterogeneous. Different sources produce data with very different characteristics. Sensor data, such as electrocardiogram and electroencephalograms have value for both real time and retrospective analysis. In supervised learning, the network is trained using a database that contains a number of examples where the input is provided along with their correct output. In medical image processing, the amount of input data for training an artificial neural network also poses a problem.