A Survey of Clustering Methods for Health Care Using Data Mining

Abstract

Due to the increasingly expanding medical profession, big data analytics has begun to play a crucial role in advancing healthcare execution and research. It has enabled the collection, management, analysis, and assimilation of huge volumes of unique, structured, and unstructured information generated by contemporary medical service systems. It has provided devices for gathering, directing, analysing, and storing vast quantities of unique, structured, and unstructured data generated by contemporary medicinal administration systems. It produces information in exponentially varied configurations. The medical services division has been well ahead of the curve in adopting this new technology, and it is producing this data at an exponential rate. Consequently, the medical services information contains a substantial amount of information originating from internal and external sources. Payers (claims and cost data), consumers and marketers (patient conduct and feeling data), providers (medical information, government population and general wellbeing information), developers (Pharmacy and therapeutic device research and development), and researchers and scientists (academic and independent) are among the information sources. Because data isn't always the same, each of these data storage facilities is also becoming more diverse, as shown by the four Vs: volume, velocity, variety, and veracity.