Anomaly Detection System For Healthcare Resource Usage In Machine Learning

  • Natarajan chellasamy Ph.D Scholar, Saveetha University, Chennai
  • DR. J. M. Gnanasekar Professor , Dept. of Computer Science and Engineering,Sri Venkateswara College Of Engineering, Chennai


Data mining approaches have been widely applied in the field of healthcare. Patient Medical Records contains vast clinical informations about patient conditions along with treatment and its procedure .Systematic healthcare utilization analysis use these observational datas to guide resource planning and improve the quality of care delivery while reducing cost. Here present a framework for utilization analysis that can be easily applied to the Diabetes population. The framework includes patients profiling with the disease entries and the patient conditions with treatment procedure, and contextual anomaly detection to provide the better healthcare delivery for the normal and abnormal patients by classifying the different kind of clinical characteristics patients in to clusters and form the patterns of disease evolution. Have to provide the corrective actions for the anomalies.


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How to Cite
CHELLASAMY, Natarajan; GNANASEKAR, DR. J. M.. Anomaly Detection System For Healthcare Resource Usage In Machine Learning. INTERNATIONAL JOURNAL OF COMPUTER & DISTRIBUTED SYSTEMS, [S.l.], v. 5, n. 1, p. 138-143, apr. 2017. ISSN 2278-5183. Available at: <>. Date accessed: 19 oct. 2017. doi: