Prediction of Cervical Cancer Using Boosting Techniques

Abstract

Cancer of the cervix, commonly called cervical cancer, is a type of cancer that develops in the cells of the cervix, which is the lower portion of the uterus that attaches to the vagina. It hardly shown symptoms in its early stage. To detect the disease, regular is required, however larger population of women not aware of this approach while many shy away and refuse to take the test. Hence cervical cancer spread like wild fire among women and being the most common cause of cancer disease it result to untimely death among women in our society today. In this research, the performance of a few sophisticated ensemble models, such as Bagging Classifier and Adaptive Boosting (AdaBoost) Classifier, is shown for the purpose of predicting a diagnosis of cervical cancer based on recorded cancer risk factors and target variables. Accuracy, sensitivity, and specificity were the measures that were used in the evaluation of the models. Python library was adopted for the classification and the cervical cancer dataset used for the experiment was acquired from UCI (University of California at Irvine), the classification was carried using voting approach by combining three classifiers: Decision Tree (DT), K-N Neighbour(KNN) and Random Forest (RF). The results indicated that the proposed model was highly accurate in predicting the risk of cervical cancer, with 119 instances classified as ‘class zero’ and only three instances classified as ‘class one’ based on the predictions.