Feature Selection Algorithm using Fuzzy Rough Sets for Predicting Cervical Cancer Risks

Abstract

Early detection or prediction is very important to reduce the fatalities of Cervical Cancer. Cancer cells affect the Cervix area initially, and then it will spread near by parts. A method using Fuzzy Rough sets is used to analyze the demographic dataset and identify the risk of Cervical Cancer. This method integrates Entropy, Information Gain (IG) and Fuzzy Rough sets for identifying the risk of Cervical Cancer earlier. Risk Factors are identified by IG. Rules are extracted by Fuzzy Rough sets. These rules can be used to identify the risk of Cervical Cancer efficiently than the decision trees. It is found that Human Papilloma Virus (HPV) and having Multiple Sexual Partners (MP) are the major risk factors increase the chances of affecting this cancer. If all the above factors are high the risk of affecting Cervical Cancer is high. Result of this paper will help to improve the clinical practice guidance for analyzing the risk of Cervical Cancer.