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Ituma Chinagolum

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Open access Aug 2026

Comparative Analysis of Decision Tree, Random Forest, SVM, Logistic Regression and XGBoost for Cervical Cancer Risk Prediction

Experimental results demonstrate that XGBoost and Random Forest achieved superior predictive performance due to their robustness against overfitting and ability to model complex feature interactions, but XGBoost is better than Random Forest with a little difference.

Agwu Joy Nneka, Ituma Chinagolum · 0 citations

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