HLPL: <u>H</u> epatic Evaluation for <u>L</u> ife <u>P</u> rediction based on Machine and Deep  <u>L</u> earning

Liver cancer survival prediction is challenged by high-dimensional, nonlinear clinical data and censoring effects. To address this, this paper proposes HLPL (Hepatic Evaluation for Life Prediction based on Machine and Deep Learning), an interpretable and scalable framework for accurate survival prediction. The proposed HLPL integrates data preprocessing, Cox-based feature selection, interpretable analysis, and machine learning (ML) and deep learning (DL) modeling. Specifically, the univariate Cox proportional hazards model is used for feature screening, while SHapley Additive exPlanations (Cox-SHAP) is applied solely for interpretability and feature refinement, without being used as a downstream model input. To capture patient heterogeneity, K-means clustering is performed exclusively on the training set, preventing data leakage. Experimental results show that the proposed HLPL framework achieves superior performance compared to conventional methods. The model attains an accuracy of 0.88. Furthermore, clustering analysis reveals distinct survival patterns across patient subgroups, while SHAP analysis identifies key prognostic factors such as tumor stage and treatment modality. These results demonstrate that HLPL provides an accurate, interpretable, and computationally efficient solution for survival prediction in liver cancer.