Enhancing Cervical Cancer Prediction: A Comparative Analysis of Machine Learning Algorithms and Development of a Novel Screening Tool
Faith Tobore Edafetanure-Ibeh · 2024
The early discovery of cervical cancer is crucial for efficient treatment and increased survival rates, making it a severe public health concern [1]. This study uses a consistent dataset to compare various machine-learning methods for cervical cancer prediction. We utilized a variety of machine learning techniques, including Random Forest, Naive Bayes, Support Vector Machine (SVM) with a linear kernel, KNearest Neighbors (KNN), Logistic Regression, and Extreme Gradient Boosting (XGBoost), to identify and forecast the risk of cervical cancer. Based on the accuracy, precision, recall, F1score, and confusion matrices, the effectiveness of these algorithms was assessed [2]. The most appropriate model for this application is XGBoost, which fared better than other models in recall and F1-score, even if more conventional methods, such as Random Forest and KNN, showed excellent overall accuracy. The study results imply that XGBoost has excellent potential for creating an efficient cervical cancer screening tool due to its balance of sensitivity and precision. The model is then integrated into a web-based application and an interactive chatbot designed to facilitate early detection and assessment of cervical cancer risks.