A recent large-scale study presents a weighted ensemble model designed to predict cardiovascular disease risk by integrating advanced machine learning techniques such as LightGBM, XGBoost, and convolutional neural networks (CNN). The model aims to deliver both reliable and interpretable predictions, addressing a critical global health issue. To enhance interpretability, the study employs methods including SHAP (SHapley Additive exPlanations) and surrogate decision trees, which help elucidate the model’s decision-making process. The research claims positive outcomes regarding the model’s effectiveness in accurately predicting heart disease risk, as well as its interpretability. This approach reflects a growing trend in applying ensemble models combined with explainability tools to improve clinical decision support. The study’s scale and methodological rigor suggest potential for practical application in healthcare settings. These findings contribute to ongoing efforts to leverage artificial intelligence for better disease prediction while maintaining transparency in model outputs.