EVALUATING MACHINE LEARNING ALGORITHMS FOR CERVICAL CANCER PREDICTION: A COMPARATIVE ANALYSIS.

The early discovery of cervical cancer is crucial for efficient treatment and increased survival rates, making it a severe public health concern (Sobar et al., 2016). This study compares various machine- learning methods for cervical cancer prediction by utilizing a consistent dataset. We utilized a variety of machine learning techniques, including Random Forest, Naive Bayes, Support Vector Machine (SVM) with a linear kernel, K-Nearest Neighbors (KNN), Logistic Regression, and Extreme Gradient Boosting (XGBoost), to identify and forecast the risk of cervical cancer. Based on the accuracy, precision, recall, F1-score, and confusion matrices, the effectiveness of these algorithms was assessed (Kourou et al., 2015). 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. To confirm these results and improve model performance for clinical applications, more excellent investigation into model optimization and evaluation on a bigger and more varied dataset is advised.Keywords: Cervical Cancer, Machine Learning, Predictive Modeling, XGBoost, Classification, Healthcare Analytics.