Comparative Analysis of Decision Tree, Random Forest, SVM, Logistic Regression and XGBoost for Cervical Cancer Risk Prediction
TL;DR
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.
Abstract
Cervical cancer remains one of the leading causes of cancer-related mortality among women worldwide, particularly in low- and middle-income countries where access to early screening and diagnosis is limited. Accurate prediction of cervical cancer threat using machine learning techniques can support early intervention, improve patient outcomes and supporting clinical decision-making. This study presents a comparative analysis of five widely used supervised machine learning algorithms such as decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR), and XGBoost or cervical cancer risk prediction. A publicly available cervical cancer dataset was preprocessed to address missing values, class imbalance, and feature scaling where necessary. The models were trained and evaluated using standard performance metrics namely; Accuracy, Precision, Recall, F1-score, and Area Under the Receiver Operating Characteristic Curve (AUC-ROC). 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. Logistic Regression provided a strong baseline with high interpretability, while SVM exhibited competitive classification performance depending on parameter optimization. Decision Tree offered transparent decision rules but was more susceptible to overfitting compared to the other models. The findings highlight the effectiveness of each algorithm and emphasize the potential of machine learning models in facilitating early identification of individuals at high risk of cervical cancer. This comparative study provides valuable insights for researchers and healthcare practitioners seeking to develop accurate and interpretable predictive models for cervical cancer screening, facilitating early detection and intervention, reducing the burden of cervical cancer.