Predictive modeling for cervical cancer detection using machine learning

Cervical cancer is a health concern of high magnitude, particularly in the areas where screening facilities are few. It is important to reduce its effects by early and accurate prediction. This study assesses different machine learning methods on predicting cervical cancer by utilizing real-world and healthcare data. Cross-validation was used to rigorously test models, and they were used to improve accuracy using ensemble methods such as stacking and voting. It was demonstrated that Multilayer Perceptron reaches 95.83% accuracy, and ensemble methods were 94.44% which was better than the traditional methods of diagnosing disease. This work is a comprehensive comparative analysis, unlike previous research that used single classifiers and very little preprocessing, and involved detailed data preparation and several algorithms. The results illustrate the power of machine learning in enhancing the early detection of cervical cancer, particularly in low resource-based practices.