Cardiovascular disease (CVD) remains the foremost cause of mortality worldwide, underscoring the urgent need for intelligent and data-driven diagnostic tools. Traditional predictive models often struggle to generalize across heterogeneous datasets and complex physiological patterns. To address this, we propose a hybrid ensemble framework that integrates deep learning architectures, Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM), with classical machine learning algorithms, including K-Nearest Neighbor (KNN) and Extreme Gradient Boosting (XGB), using an ensemble voting mechanism. This approach combines the representational power of deep networks with the interpretability and efficiency of traditional models. Experiments on two publicly available Kaggle datasets demonstrate that the proposed model achieves superior performance, reaching 82.30 percent accuracy on Dataset I and 97.10 percent on Dataset II, with consistent gains in precision, recall, and F1-score. These findings underscore the robustness and clinical potential of hybrid AI frameworks for predicting cardiovascular disease and facilitating early intervention. Furthermore, this study directly supports the United Nations Sustainable Development Goal 3 (Good Health and Well-being) by promoting early diagnosis, prevention, and management of non-communicable diseases through innovative, data-driven healthcare solutions.

翻译：心血管疾病（CVD）仍然是全球最主要的死亡原因，这凸显了对智能化和数据驱动诊断工具的迫切需求。传统的预测模型在处理异构数据集和复杂生理模式时往往难以实现良好的泛化能力。为解决这一问题，我们提出了一种混合集成框架，该框架通过集成投票机制，将深度学习架构——卷积神经网络（CNN）和长短期记忆网络（LSTM）——与经典机器学习算法（包括K近邻算法（KNN）和极限梯度提升（XGB））相结合。这种方法融合了深度网络的表征能力与传统模型的可解释性和效率。在两个公开的Kaggle数据集上进行的实验表明，所提出的模型取得了优异的性能，在数据集I上达到82.30%的准确率，在数据集II上达到97.10%的准确率，并且在精确率、召回率和F1分数上均获得了一致的提升。这些发现强调了混合人工智能框架在预测心血管疾病和促进早期干预方面的鲁棒性和临床潜力。此外，本研究通过创新的、数据驱动的医疗解决方案促进非传染性疾病的早期诊断、预防和管理，从而直接支持了联合国可持续发展目标3（良好健康与福祉）。