Improved Classification Accuracy for Identification of Cervical Cancer

Abstract

The major purpose of this research is to forecast cervical cancer, compare which algorithms perform well, and then choose the best algorithm to predict cervical cancer at an early stage. Cervical cancer classification can be automated using a machine learning system. This study evaluates multiple machine learning techniques for cervical cancer classification. For this classification, algorithms such as Decision Tree, Naive Bayes, KNN, SVM, and MLP are proposed and evaluated. The cervical cancer Dataset, which was retrieved from the UCI machine learning data repository, was used to test these methods. With the help of Sciklit-learn, the algorithms' results were compared in terms of Accuracy, Sensitivity, and Specificity. Sciklit-learn is a Python-based machine learning package that is available for free. Finally, the best model for predicting cervical cancer is developed.