The School of Computer Science & Information Engineering, Shanghai Institute of Technology, Shanghai 201418, China.
Sensors (Basel). 2024 Nov 7;24(22):7160. doi: 10.3390/s24227160.
Cardiovascular diseases (CVD) have become a major public health problem affecting the national economy and social development, and have become one of the major causes of death. Therefore, the prevention, control and risk assessment of CVD have been increasingly emphasized. However, current CVD prediction models face limitations in capturing complex relationships within physiological data, potentially hindering accurate risk assessment. This study addresses this gap by proposing a novel Framework for Multi-Input, One-dimensional Convolutional Neural Network (1D-CNN) with Attention Mechanism for CVD (FMI-CAECD). This framework leverages the feature extraction capabilities of Convolutional Neural Network (CNN) alongside an Attention Mechanism to adaptively identify critical features and non-linear relationships within the data. Additionally, Shapley Additive Explanations (SHAP) analysis is incorporated to provide deeper insights into individual feature importance for disease prediction. Performance evaluation on the BRFSS 2022 dataset demonstrates that FMI-CAECD achieves superior accuracy (97.45%), sensitivity (96.84%), specificity (95.07%), and F1-score (92.44%) compared to traditional machine learning baselines and other deep learning models. These findings suggest that FMI-CAECD offers a promising approach for CVD risk assessment.
心血管疾病（CVD）已成为影响国民经济和社会发展的重大公共卫生问题，也是主要死亡原因之一。因此，CVD 的预防、控制和风险评估越来越受到重视。然而，目前 CVD 预测模型在捕捉生理数据内部复杂关系方面存在局限性，可能会阻碍准确的风险评估。本研究通过提出一种新的多输入一维卷积神经网络（1D-CNN）与注意力机制的心血管疾病预测框架（FMI-CAECD）来解决这一差距。该框架利用卷积神经网络（CNN）的特征提取能力以及注意力机制，自适应地识别数据中的关键特征和非线性关系。此外，还引入了 Shapley Additive Explanations（SHAP）分析，以深入了解单个特征对疾病预测的重要性。在 BRFSS 2022 数据集上的性能评估表明，与传统机器学习基线和其他深度学习模型相比，FMI-CAECD 在准确性（97.45%）、敏感性（96.84%）、特异性（95.07%）和 F1 分数（92.44%）方面表现优异。这些发现表明，FMI-CAECD 为 CVD 风险评估提供了一种有前途的方法。
