Intelligent Lifecycle Architecture of: Disease Diagnosis Based Adapted CGA Using SOA

Development of concepts and techniques to help solve the disease misdiagnosis problems is the focal point of numerous challenges that medical and health care professionals encounter. The study of this research creates an Intelligent Medical Extended Information Retrieval Lifecycle Architecture (IMEIRLA) based Adapted Clustering Genetic Algorithm (ACGA) using Service-Oriented Architecture (SOA). By increasing the accuracy of disease diagnosis, the proper treatment can be administered sooner therefore potentially shortening the time of stay for the patient through accelerated information retrieval. It also eliminates unnecessary testing. In this research, the concept of genetic algorithm based clustering has been adapted and applied to provide better clustering results in a more efficient manner. Clustering the results of Medical and Health care search engines gives extra information that is vital to a Medical and Health care professionals’ patients’ recovery. The study for this research builds utility-based intelligent agents to minimize amount of time it spends clustering results. The agents maximize performance of the solution by making it smarter