Project Abstract: Cervical cancer remains a significant health issue worldwide, making early detection crucial for better patient outcomes. Using patient data, this project applies a machine-learning approach to predict cervical cancer. The focus is on an XGBoost classifier, a powerful ensemble technique, to model the likelihood of cervical cancer based on various clinical and demographic factors. After extensive preprocessing, including handling missing data, normalization, and removing irrelevant features, exploratory data analysis (EDA) was conducted to reveal patterns and correlations. Visual tools like heatmaps were employed to visualize relationships within the data. The trained XGBoost model was evaluated using performance metrics such as the confusion matrix, demonstrating its effectiveness in distinguishing positive and negative cervical cancer cases. The findings underscore the value of machine learning in early cancer detection, highlighting the critical role of data quality and feature selection in healthcare AI.
Project Keyword: Cervical Cancer Detection, Machine Learning, Xgboost Classifier, Predictive Analytics, Healthcare AI