ABSTRACT
Most of the hospitals have accumulated a large amount of medical and treatment data after many years of operation. Moreover, data are still being generated in hospitals every day, which contain valuable information. How to extract and fully utilize the value of data from the massive historical hospital data is a key issue for both hospitals and society. With the arrival of the big data and cloud computing era, this issue has attracted considerable attention. In addition, the speed of data mining 404and analysis for large-scale data becomes a hot topic to researchers from both academia and industry. In this chapter, we focus on the optimization methods for parallel data-mining algorithms, and the applications of these algorithms in the field of large-scale hospital data processing. First, techniques of data mining and famous cloud computing platforms are considered. Then, different parallel optimization methods of the related data-mining algorithm are discussed, such as the parallel random forest algorithm based on an Apache Spark platform. Finally, applications of big data processing in hospitals are discussed, based on the optimized data-mining algorithms in the big data and cloud computing environment.
