Hepatocellular Carcinoma Patient’s Survival Prediction Using Oversampling and Machine Learning Techniques

Hepatocellular Carcinoma (HCC) is the most crucial liver neoplasm and the subsequent driving reason for malignant growth demise worldwide. The survival rates for patients determined to have HCC and to distinguish prognostic components, which will help in picking ideal treatment for singular patients. Throughout the long term, and for the specific instance of HCC, some investigation contemplates have been creating procedures for helping physicians, employing machine learning methods to anticipate the survival rate after treatment. In this paper, we have applied machine learning calculations to anticipate the 1-year endurance of patients and discover the feature importance. We have employed an oversampling strategy named SMOTE, for adjusting the dataset to improve the model performance. We have shown the difference in model performance before and after using SMOTE. Our proposed system with the XGBoost classifier performed better with 87% accuracy in comparison with other existing models.