An Early Detection Method of Type-2 Diabetes Mellitus in Public Hospital

Diabetes is a chronic disease and major problem of morbidity and mortality in developing countries. The International Diabetes Federation estimates that 285 million people around the world have diabetes. This total is expected to rise to 438 million within 20 years. Type-2 diabetes mellitus (T2DM) is the most common type of diabetes and accounts for 90-95% of all diabetes. Detection of T2DM from various factors or symptoms became an issue which was not free from false presumptions accompanied by unpredictable effects. According to this context, data mining and machine learning could be used as an alternative way help us in knowledge discovery from data. We applied several learning methods, such as instance based learners, naive bayes , decision tree, support vector machines, and boosted algorithm acquire information from historical data of patient’s medical records of Mohammad Hoesin public hospital in Southern Sumatera. Rules are extracted from Decision tree to offer decision-making support through early detection of T2DM for clinicians.