부산대학교 도서관

Nowadays technology has improved the worldwide and has become vital part of our life. It aid for doctors to analyze and diagnose the medical problems and diseases. With help artificial intelligence in medicine science become high demand now. This work focuses on clinical decision support system which aid medical people to diagnose of disease. In this paper first present related work in various aspects of clinical decision support systems to provide diagnosis solutions to medical related problems. In this paper a proposed method to identify patient with diabetes disease risk level is indentified. In this work diabetes patient risk level is been detected by using ontology and machine learning technique. Ontology holds disease symptoms, causes and treatments. In machine learning, nave base algorithm is used to make decision on patient record also it defines possibilities of risk level. The proposed algorithm will be evaluated against the following metrics namely confusion matrix, precision level, mean and this proposed work is found to have better prediction level when compared with existing work.