Optimizing Cervical Cancer Diagnosis with Correlation-Based Feature Selection: A Comparative Study of Machine Learning Models

Abstract

Cervical cancer remains a significant global health issue, particularly in developing countries where it is a leading cause of mortality among women. The development of machine learning-based approaches has become essential for early detection and diagnosis of cervical cancer. This research explores the optimization of classification algorithms through Correlation-Based Feature Selection (CFS) for early cervical cancer detection. A dataset consisting of 198 samples and 22 attributes from medical records was processed to reduce dimensionality. CFS was used to select the most relevant features, which were then applied to three classification algorithms: Naïve Bayes, Decision Tree, and k-Nearest Neighbor (k-NN). The results showed that CFS significantly improved classification accuracy, with Decision Tree achieving the highest accuracy of 85.89%, followed by Naïve Bayes with 83.34%, and k-NN with 82.32%. These findings demonstrate the importance of feature selection in enhancing classification performance and its potential application in the development of cervical cancer detection tools.