| 张汇文,杨晓霞,高娜,王宁,张翠翠.融合注意力残差机制的BP神经网络在冠状动脉疾病诊断中的应用[J].电子测量与仪器学报,2025,39(9):192-201 |
| 融合注意力残差机制的BP神经网络在冠状动脉疾病诊断中的应用 |
| Application of BP neural network with attention residual mechanismin the diagnosis of coronary artery disease |
| |
| DOI： |
| 中文关键词: 冠状动脉疾病 深度学习 特征选择 BP神经网络 残差结构 注意力机制 |
| 英文关键词:coronary artery disease deep learning feature selection BP neural network residual structure attention mechanism |
| 基金项目:国家重点研发计划项目（2022YFC3104600）、国家自然科学基金（62001329）、天津市科技计划项目（24YDTPJC00740）、海南省重点研发计划项目（ZDYF2024SHFZ051）资助 |
|
|
| Author | Institution |
| Zhang Huiwen | Tianjin Key Laboratory of Information Sensing and Intelligence Control, Tianjin University of Technology and Education,
Tianjin 300222, China |
| Yang Xiaoxia | Tianjin Key Laboratory of Information Sensing and Intelligence Control, Tianjin University of Technology and Education,
Tianjin 300222, China |
| Gao Na | Tianjin Key Laboratory of Information Sensing and Intelligence Control, Tianjin University of Technology and Education,
Tianjin 300222, China |
| Wang Ning | Tianjin Key Laboratory of Information Sensing and Intelligence Control, Tianjin University of Technology and Education,
Tianjin 300222, China |
| Zhang Cuicui | School of Marine Science and Technology, Tianjin University, Tianjin 300072, China |
|
| 摘要点击次数: 558 |
| 全文下载次数: 501 |
| 中文摘要: |
| 在医疗健康领域, 冠状动脉疾病作为心脏病的一种, 对人民的生命健康造成严重威胁。但由于导致冠状动脉疾病发病的影响因素较为复杂, 且在发病初期存在隐匿性, 导致多数患者错过最佳治疗时段而无法痊愈甚至出现更悲剧的结果。为帮助人群在发病初期进行准确的诊断预测并提供相应的预防措施和治疗手段, 各类基于机器学习诊断算法被广泛应用于医疗健康领域。目前, 随着人工智能领域的快速发展, 深度学习也被逐渐应用于心脏病的诊断预测中。通过对克利夫兰心脏病数据集中的数据分布进行可视化, 通过特征选择, 揭示特定医学指标与冠状动脉疾病之间的紧密联系, 以探索关键因子对冠状动脉疾病发病的影响趋势。构建基于融合注意力与残差机制的BP神经网络的冠状动脉疾病诊断预测模型, 模型通过残差结构缓解梯度消失, 保持梯度流动的稳定性, 并通过多头注意力机制捕捉特征间的深层依赖关系, 实现特征权重的动态分配。该数据集在冠状动脉疾病诊断模型上进行训练获得97.1%的准确率。为了验证方法的有效性, 将其与现有机器学习算法进行比较评估并进行对比实验, 发现该算法在关键性能指标上均展现出更为优越的表现, 验证了其在医疗健康领域, 尤其是在心脏病诊断中的实际应用价值。 |
| 英文摘要: |
| Coronary artery disease (CAD), as one of typical heart diseases, threatens people’s lives and health. However, due to its complex influencing factors and its subtle initial symptoms, many patients miss the optimal treatment window for recovery. To enable early diereses prevention so as to get most appropriate treatment, many machine learning methods have been widely applied in this field, among which deep learning has been acknowledged as one of the cutting-edge techniques for CAD diagnosis. This paper develops a tailored BP neural network, which integrates BP with an attention-residual-mechanism for CAD detection. In order to find the key factors that contribute to CAD prediction, we fist investigate a feature selection strategy based on data visualization and using several statistical methods on the commonly used cleveland heart disease dataset. Then, the attention-residual-mechanism informed BP network is conducted for CAD detection. The amended BP network alleviates the gradient vanishing problem by using a residual structure and captures deep dependencies between features through a multi-head attention mechanism, which can be used for dynamic allocation of feature weights. Extensive experiments demonstrate the better performance of our method than existing machine learning algorithms. It can achieve an accuracy of 97.1% on Cleveland Heart Disease dataset, which verifies the effectiveness of our method in CAD diagnosis. |
| 查看全文 查看/发表评论 下载PDF阅读器 |
|
|
|
