A Survey of Ontologies on Disease Classification
In medical ontology, it is often difficult to establish such definitions for diseases classification. This paper addresses the problem of classifying the disease based on medical ontology. Our goal is to provide a method for improving the classification of medical ontology which will allow us to generate a new representation based on concepts. There are many techniques used in medical ontology they are case profile ontology, decision support tool, particle swarm optimization model, etc. To evaluate the effectiveness of the intelligent system, three benchmark medical data sets, viz. Breast Cancer Wisconsin, Pima Indians Diabetes, and Liver Disorders from the UCI Repository of Machine Learning, are used for evaluation. A number of useful performance metrics in medical applications which include accuracy, sensitivity, specificity are computed. The results are analyzed and compared with those from other methods published in the literature. The experimental outcomes positively demonstrate that the hybrid intelligent system is effective in undertaking medical data classification tasks.