Abstract:
Rough set theory is based on the establishment of equivalence classes within the given training data. All of the data samples forming an equivalence class are indiscernible, that is, the samples are identical with respect to the attributes describing the data. The application of the rough set theory to identify the most important attributes and to induce decision rules from the medical data set with diabetes mellitus are discussed in this paper. Rough set theory can be used for classification to discover structural relationships within imprecise or noisy data. Hence, a medical information system for diabetes mellitus is developed using Rough Set Theory. Diabetes is a serious and rapidly escalating global health problem and one of the leading causes of death. Diabetes is caused by a defect in insulin secretion, insulin action, or both. Information system is needed to have patients who test themselves at home instead of clinical test. Applying Rough set , this system can be used for prediction or classification problem in the medical domain and shown high performance.
