Ontology-Based Decision Supporting System for Diagnosis and Treatment of Leukemia

Abstract

Decision supporting system for Diagnosis of diseases is a process of assessing all possible set of symptoms to identify the disease in order to recommend treatment carried out by the doctors (physician) all over the world. But how an expert interprets the patient's condition may be different due to the knowledge handling problem which is tedious and very time consuming and mainly leads to a wrong decision. As well, the number of doctors and hospitals are not matched with the number of patients in the community. However, this paper uses semantic technology concept for building ontology, knowledge repository, which helps leukemia patient to diagnosis and get medical recommendations by inferring types of disease and its specific recommendation that help a patient to counteract the disease. The proposed system uses protégé tools to develop ontology. Moreover, a well-known evaluation method such as accuracy, precision, recall, and F-measure are used to evaluate the effectiveness and efficiency of the model. Based on the performed evaluation the proposed ontology-based model achieved 95% precision, 94.8% recall, 94.7% F1-score, and 94.1% accuracy.