Big Data Analytics in Health Care: A Review Paper

Abstract

The application of big data in health care is a fast-growing field, with many discoveries and methodologies published in the last five years. Big data refers to datasets that are not only big but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. Moreover, medical data is one of the most growing data, as it is obtained from Electronic Health Records (EHRs) or patients themselves. Due to the rapid growth of such medical data, we need to provide suitable tools and techniques in order to handle and extract value and knowledge from these datasets to improve the quality of patient care and reduces healthcare costs. Furthermore, such value can be provided using big data analytics, which is the application of advanced analytics techniques on big data. This paper presents an overview of big data content, sources, technologies, tools, and challenges in health care. It also intends to identify the strategies to overcome the challenges.

Breast cancer diagnosis using machine learning techniques

“…In this context, machine learning (ML) techniques offer unique opportunities to create automated systems capable of analyzing complex medical data and predicting the likelihood of tumour malignancy. One important tool in this direction is the Breast Cancer Wisconsin dataset, which contains the characteristics of cell nuclei obtained from breast biopsies [1].…”