A Cervical Cancer Prediction Model Using REPTree Classifier

Cervical cancer is the foremost gynecological disease globally. In this manuscript, we build up a Cervical Cancer prediction model that can aid medical experts in envisaging Cervical Cancer condition based on the clinical data of patients. At the outset, we choose 32 imperative clinical attributes viz., age, hormonal contraceptives, number of sexual partners, STDs: AIDS, first sexual intercourse (age), STDs: HIV, number of pregnancies, STDs: Hepatitis B, smokes etc., in addition to four classes (Hinselmann, Schiller, Cytology and Biopsy). Secondly, we build up a prediction model by means of REPTree classifier for classifying Cervical Cancer based on these clinical attributes against unpruned, and pruned error pruning approach. As a final point, it is concluded that the precision of unpruned REPTree classifier with Pruned REPTree classifier approach is better than the Pruned REPTree classifier approach. The outcome acquired that which illustrates that age, hormonal contraceptives, first sexual intercourse (age), STDs: genital herpes, number of pregnancies and smokes are the foremost predictive attributes which provides enhanced classification in opposition to the supplementary attributes.