Description

Diabetes is part of the growing epidemic of non-communicable diseases, with a high burden for the society on developing countries in future. For suppressing the development of diabetes mellitus and the onset of complications to manage their healthcare or personal data the system aim to apply association rule mining to electronic medical records to discover sets of risk factors. The four methods summaries the high risk of diabetes. The extension to the bottom up summarization algorithm produced the most suitable summary. Adjusted for confounders, advancing age, rural-urban migration, physical inactivity, smoking, abstinence of alcohol, low intake of fruits-vegetables, family history of DM, refined sugar intake, high social class, high intake of animal fat and protein, and stress, were the independents determinants of all cases of DM. Extended four popular associa-tion rule set summarization techniques by incorporating the risk of diabetes into the process of finding an optimal summary. The performance of the test is also less than ideal. As a specific example, consider the accuracy of the FPG test at a threshold of 126 mg/dL when using the OGT test as the determinant of truth