Machine Learning-Based Cervical Cancer Prediction:A Systematic Review of Ensemble Methods and Explainable AI Approaches

Cervical cancer remains a significant public health challenge, particularly in low-resource settings where early diagnosis is often limited. This study presents a machine learning-based framework for the early prediction of cervical cancer using clinical and screening data. Multiple classifiers, including Support Vector Machines (SVM), Random Forest, Logistic Regression, XGBoost, and ensemble models, are systematically evaluated to identify the most effective predictive approach. The proposed methodology incorporates robust data preprocessing, feature selection, and class imbalance handling techniques to enhance model performance. In addition, explainable artificial intelligence (XAI) methods are employed to improve model interpretability and support clinical decision-making. Experimental results demonstrate that ensemble-based models achieve superior accuracy, stability, and generalization compared to individual classifiers. The study further highlights the importance of integrating clinically relevant features to improve predictive reliability. Despite promising outcomes, challenges such as limited dataset diversity and lack of multi-center validation remain. The proposed framework demonstrates strong potential as a scalable and interpretable decision-support system for early cervical cancer detection and risk assessment.