Diabetes Mellitus Estimation Risk Using Association rule Mining

Abstract

Diabetes is part of the growing epidemic of non communicable diseases. Early detection of patients with elevated risk of developing diabetes mellitus is critical to the improved prevention and overall clinical management of these patients. Aim to apply association rule mining to electronic medical records (EMR) to discover sets of risk factors. Given the high dimensionality of EMRs, association rule mining generates a very large set of rules which we need to summarize for easy clinical use. The four methods summaries the high risk of diabetes.

Authors and Affiliations

Prita Parshwanath Dongaonkar, Ms. Ashwini Gaikwad

Keywords

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  • EP ID EP20439
  • DOI -
  • Views 257
  • Downloads 4

How To Cite

Prita Parshwanath Dongaonkar, Ms. Ashwini Gaikwad (2015). Diabetes Mellitus Estimation Risk Using Association rule Mining. International Journal for Research in Applied Science and Engineering Technology (IJRASET), 3(5), -. https://europub.co.uk/articles/-A-20439