Intelligent optimization models for disease diagnosis using a service-oriented architecture and management science

Abstract

The accuracy of disease diagnosis remains a significant challenge that medical and health care industries experience due to a relative lack of misdiagnosis studies and a difficulty of retrieving patients' information. Validation of diagnosis and certainty of its accuracy is the goal of this research. The research, as reported in this paper introduces an innovative solution to determine the accuracy of disease diagnosis. The solution is based on Intelligent Optimization Models (IOM) using integration of Service-Oriented Architecture (SOA) and Management Science (MS). These models enable medical doctors to make inference about disease diagnosis and allow a quick diagnosis of diseases at higher level of accuracy. The models also have the advantage of reducing health risk associated with experimenting with real patients. In particular, bad decisions that cause death or wrong treatment can be avoided. About 44,000 to 98,000 Americans die annually as the result of medical errors. Experimenting with these models requires less time and is less expensive than experimenting with studying patient's condition. In a SOA environment, the study of this research develops new intelligent concepts. These concepts integrate approaches of management science models including linear programming and network, search methodologies, information retrieval, clustering extended genetic algorithm, and intelligent agents. A prototype is created and examined in order to validate the concepts. The proposed concepts strengthen the capacity and quality of STEM undergraduate degree programs. The concepts also promote a vigorous STEM academic environment to increase the number of students entering STEM careers.