Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, highlighting the critical need for accurate and interpretable early risk prediction. While conventional tools such as the Framingham Risk Score offer high transparency, they rely on rigid assumptions and fixed variable sets, limiting their adaptability to patient-specific variations. Recent machine learning approaches improve predictive performance but often lack interpretability, hindering clinical acceptance. To address these limitations, we propose CluRF, a hybrid framework that combines unsupervised clustering with Random Forest classifiers and integrates SHAP-based explanations. CluRF partitions the population into latent subgroups based on feature similarity, enabling personalized modeling within each cluster and providing global and individualized interpretability. We evaluate our method on four real-world heart disease datasets and demonstrate that CluRF achieves competitive or superior performance compared to state-of-the-art models in terms of F1-score, accuracy, and AUC. Importantly, it maintains a lightweight and explainable structure suitable for clinical deployment.