This research focuses on predicting the life expectancy of lung cancer patients after undergoing thoracic surgery, using a decision tree classification algorithm (C4.5) combined with adaptive synthetic sampling (ADASYN) to address data imbalance. Data imbalance in the lung cancer patient dataset is a significant challenge in obtaining accurate prediction results, particularly in identifying minority classes. By applying ADASYN, the data distribution becomes more balanced, improving the performance of the C4.5 model. The results showed that combining these methods increased the prediction accuracy from 67% to 87%. Additionally, precision, recall, and F1-score for minority classes significantly improved, which were previously difficult for the model to identify. Therefore, the combination of the C4.5 algorithm and ADASYN technique proves effective in addressing the challenge of data imbalance and achieving better predictions in the case of lung cancer. This study is expected to contribute to the field of medical classification and serve as a reference for future research on similar cases.