Lilhore Umesh Kumar, Simaiya Sarita, Khan Monish, Alroobaea Roobaea, Baqasah Abdullah M, Alsafyani Majed, Alhazmi Afnan
Department of Computer Science and Engineering, Galgotias University, Greater Noida, UP, India.
Arba Minch University, Arba Minch, Ethiopia.
Sci Rep. 2025 Jul 12;15(1):25273. doi: 10.1038/s41598-025-09594-8.
Heart disease remains the leading cause of death globally, mainly caused by delayed diagnosis and indeterminate categorization. Many of traditional ML/DL methods have limitations of misclassification, similar features, less training data, heavy computation, and noise disturbance. This study proposes a novel methodology of Modified Multiclass Attention Mechanism based on Deep Bidirectional Long Short-Term Memory (M2AM with Deep BiLSTM). We propose a novel model that incorporates class-aware attention weights, which dynamically modulate the focus of attention on input features according to their importance for a specific heart disease class. With an emphasis on the informative data, M2AM can improve feature representation and well-cure the problems of mis-classification, overlapped features, and fragility against noise. We utilized a large dataset of 6000 samples and 14 features, resulting in noticeable noise reduction from the MIT-BIH and INCART databases. Applying an Improved Adaptive band-pass filter (IABPF) to the signals resulted in noticeable noise reduction and an enhancement of signal quality. Additionally, wavelet transforms were employed to achieve accurate segmentation, allowing the model to discern the complex patterns present in the data. The proposed mechanism achieved high performance in the performance metrics, with accuracy of 98.82%, precision of 97.20%, recall of 98.34%, and F-measure of 98.92%. It surpassed methods such as the Classic Deep BiLSTM (SD-BiLSTM), and the standard approaches of Naive Bayes (NB), DNN-Taylos (DNNT), Multilayer perceptron (MLP-NN) and convolutional neural network (CNN). This work provides a solution to significant limitations of current methods and improves the accuracy of classification, indicating substantial progress in accurate diagnosis of heart diseases.
心脏病仍然是全球主要的死因，主要原因是诊断延迟和分类不明确。许多传统的机器学习/深度学习方法存在误分类、特征相似、训练数据少、计算量大和噪声干扰等局限性。本研究提出了一种基于深度双向长短期记忆的改进多类注意力机制（M2AM with Deep BiLSTM）的新方法。我们提出了一种新颖的模型，该模型结合了类感知注意力权重，根据输入特征对特定心脏病类别的重要性动态调整注意力焦点。通过强调信息丰富的数据，M2AM可以改善特征表示，并很好地解决误分类、特征重叠和抗噪声脆弱性等问题。我们使用了一个包含6000个样本和14个特征的大型数据集，显著降低了来自麻省理工学院-比哈尔（MIT-BIH）和心血管造影剂再注射（INCART）数据库的噪声。对信号应用改进的自适应带通滤波器（IABPF），显著降低了噪声并提高了信号质量。此外，采用小波变换实现了精确分割，使模型能够识别数据中存在的复杂模式。所提出的机制在性能指标上表现出色，准确率为98.82%，精确率为97.20%，召回率为98.34%，F值为98.92%。它超过了经典深度双向长短期记忆（SD-BiLSTM）以及朴素贝叶斯（NB）、深度神经网络-泰勒（DNNT）、多层感知器（MLP-NN）和卷积神经网络（CNN）等标准方法。这项工作解决了当前方法的重大局限性，提高了分类准确率，表明在心脏病的准确诊断方面取得了实质性进展。
