A prediction model based on machine learning: prognosis of HBV-induced HCC male patients with smoking and drinking habits after local ablation treatment.

Background: Liver cancer, particularly hepatocellular carcinoma (HCC), is a major health concern globally and in China, possibly shows recurrence after ablation treatment in high-risk patients. This study investigates the prognosis of early-stage male HCC patients with chronic hepatitis virus B (HBV) infection who also have long-term smoking and drinking habits, following local ablation treatment. Methods: Data from 257 patients treated at Capital Medical University, Beijing Youan Hospital from 2014 to 2022 were retrospectively analyzed. We first screened the variables by Lasso regression and random survival forest (RSF), followed by multivariate Cox regression analysis. Based on the screened variables after these steps, we performed and validated a nomogram to predict the survival status of these patients. Results: Our results indicated that monocytes and globulin are risk factors while pre-albumin (PALB) is protective after selected by Lasso, RSF and multivariate Cox regression, providing a robust tool for predicting overall survival and guiding treatment for high-risk HCC patients. With promising discrimination, accuracy and clinical applicability, our model was translated into a nomogram for practical use. Conclusion: Our prognostic model effectively identifies key risk factors such as monocytes, globulin and PALB, providing accurate predictions for HBV-induced male patients with smoking and drinking habits.