Effective Analytics on Healthcare Big Data Using Ensemble Learning
Pau Suan Mung, Sabai Phyu
Abstract
Healthcare big data is a collection of record of patient, hospital, doctors and medical treatment and it is so large, complex, distributed and growing so fast that this data is difficult to maintain and analyze using some traditional data analytics tools. To solve this difficulties, some machine learning tools are applied on such big amount of data using big data analytics framework. In recent years, many researchers have proposed some machine learning approaches on healthcare data to improve the accuracy of analytics. These techniques were applied individually and compared their results. To get better accuracy, this paper proposes one machine learning approach called ensemble learning, in which the results of three machine learning algorithms are combined. Soft voting method is used for combining accuracies. From these results, it is observed that ensemble learning can obtain maximum accuracy.