EARLY LUNG CANCER PREDICTION USING CTGAN AND TREE-BASED MACHINE LEARNING TECHNIQUES

Abstract

One of the main causes of cancer-related fatalities globally is still lung cancer, and increasing survival rates depends on early identification. In this work, a machine learning-based method for predicting lung cancer utilising clinical and lifestyle data from surveys is presented. The Synthetic Minority Over-sampling Technique (SMOTE) was used to address the dataset's class imbalance and guarantee equitable representation of both classes. Logistic regression was used as the meta-learner in a stacked ensemble model that combined CatBoost, XGBoost, LightGBM, AdaBoost, and Random Forest. The suggested methodology outperformed individual classifiers and came close to state-of-the-art performance documented in the literature, with an accuracy of 96.9% and a ROC AUC of 0.99. A Flask-based web application that offers an intuitive interface for prediction, visualisation, and outcome interpretation was also developed. Modules for user registration, input-based cancer risk prediction, and graphical display of test data results are all included in the system. The findings show that ensemble learning provides a reliable and user-friendly method for lung cancer prediction when combined with efficient preprocessing and web deployment.