A NEW STAD MODEL TO PREDICT THE DIABETES MELLITUS

Abstract

Diabetes-mellitus refers to the metabolic disorder that happens due to less insulin secretion action. It is characterized by hyperglycemia. The persistant hyperglycemia of diabetes leads to damage, malfunction and failure of different organs such as kidneys, eyes, nerves, blood vessels and heart. Detection and diagnosis of diabetes at an early stage is the need of the day. Diabetes disease diagnosis and interpretation of the diabetes data is an important classification problem. A variety of data mining techniques are used to discover new patterns of disease and promote the early detection and diagnosis of complex diseases such as diabetes Rule extraction is on among them. The rules are extracted from the dataset. The extracted rules may not only be highly accurate, but also simple and easy to understand. Therefore in this study, The rule extraction algorithm Enhanced STAD model is proposed to achieve highly accurate, concise, and interpretable classification rules for the pima Indian diabetes(PID) dataset, which comprises 768 samples with two classes(diabetes or non-diabetes) and eight attributes. The advanced decision tree algorithm is generated and used for classification. STAD model achieved substantially better accuracy and provided a considerably fewer average number of rules and antecedents. These results suggest that proposed algorithm, is more suitable for medical decision making including the diagnosis of all type of diabetes mellitus.