An Analytical Study on Early Diagnosis and Classification of Diabetes Mellitus

Diabetes mellitus (DM) is a chronic, general, life-threatening syndrome occurring all around the world. It is characterized by hyperglycemia occurring due to abnormalities in insulin secretion which would in turn result in irregular raise of glucose level. In recent years, the impact of Diabetes mellitus has increased to a great extent especially in developing countries like India. This is mainly due to the irregularities in the food habits of several IT professionals. Thus, early diagnosis and classification of this deadly disease has become an active area of research in the last decade. A number of techniques have been developed to deal with his disease. Numerous clustering and classifications techniques are available in the literature to visualize temporal data to identifying trends for controlling diabetes mellitus. This survey presents an analytical study of several algorithms which diagnosis and classifies Diabetes mellitus data effectively. The existing algorithms are analyzed thoroughly to identify their advantages and limitations. The performance evaluation of the existing algorithms is carried out to determine the best approach. A best approach among the existing approach is determined and a solution is also suggested to improve the overall performance of diagnosis process