Towards an intelligent decision-making support in health information systems: Lightweight hybrid data mining class library for object-oriented applications

摘要

Health Science has been developing and incorporating new techniques capable of acquiring data of patients. These data is evaluated to understand a physical condition, and its interpretation depends on the physician knowledge and experience. This process can involve uncertainty, because of the used information such as patient information or a particular disease. Diagnosis is a complicated process, but fundamental for medicine. With the advance of technology, and in particularly data mining techniques, some systems have been incorporating techniques that can handle mass group of data. This with the porpuse to help in the decision-making process in diagnosis estimation and applications. In this paper, we present a lightweight data mining class library work-in-progress. This library is currently been developed to be used in intelligent decision-making support systems, which incorporates a group of well-known hybrid data mining techniques that had been used in health science to handle big data. It is a lightweight class library that could be used by programmers to incorporate into health information systems object-oriented applications.