Comparative Survey on Data Mining Techniques for Endometrial Cancer Diagnosis and Prediction

  • A Hency Juliet Research Scholar, Research & Development Centre, Bharathiar University, Coimbatore & Assistant Professor in Department of Computer Application, Mar Gregorios College of Arts and Science, Chennai, Tamil Nadu, India
  • R Padmajavalli Research Supervisor, Research & Development Centre, Bharathiar University, Coimbatore & Associate Professor in Department of Computer Application, Bhaktavatsalam Memorial College for Women, Chennai
Keywords: Data mining, classification, clustering, association, regression, endometrial


Data Mining plays a vital role for uncovering innovative developments in healthcare society which in turn helpful for all the parties associated with this field. This study scrutinizes the efficacy of a range of Data Mining techniques such as classification, clustering, association, regression in health care realm. Cancer is one of the key predicament today, diagnosing cancer in prior period is still exigent for doctors. Identification of genetic and natural factors is very noteworthy in developing novel methods to detect andprevent cancer. Endometrial cancer is the most extensive feminine gynecologic malignant cells, is typically a curable disease. It is the most widespread of all cancers and the leading cause of cancer demises in the world of women. In this paper we have presented a survey on the data mining techniques for the endometrial cancer diagnosis and prediction, analysis of the threat aspects and the survivability rate of endometrial cancer patients.

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