Key result
Multimodal framework integrating ECG and clinical data achieves ~97% diagnostic accuracy, outperforming unimodal approaches.
Why the study?
Heart disease is a major cause of mortality worldwide, making early and accurate diagnosis essential.
Does a multimodal framework integrating ECG signals and clinical data improve heart disease classification accuracy compared to unimodal approaches?
Does a multimodal framework integrating ECG signals and clinical data improve heart disease classification accuracy compared to unimodal approaches?
A multimodal machine learning approach combining ECG signals and clinical text data achieves high accuracy (96.77%) in heart disease classification, outperforming unimodal models.
Heart disease is a major cause of mortality worldwide, making early and accurate diagnosis essential. A multimodal framework for heart disease classification is presented through the integration of electrocardiogram (ECG) signals and clinical data. ECG recordings are obtained from the PTB-XL dataset, which includes clinical attributes such as age, gender, and diagnostic labels. A Residual Neural Network (ResNet) is employed to extract discriminative features from ECG signals, while Bidirectional Encoder Representations from Transformers (BERT) is utilized to encode clinical text data and capture contextual dependencies. The extracted features are fused using a fully connected architecture to enhance classification performance. Experimental results demonstrate an accuracy of 96.77%, indicating improved performance over unimodal approaches and supporting reliable clinical decision-making.
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K et al. (2026) studied Heart disease. Multimodal framework integrating ECG signals and clinical data vs. Unimodal approaches was evaluated on Classification accuracy. A multimodal framework integrating ECG signals and clinical data achieved a classification accuracy of 96.77%, demonstrating improved performance over unimodal approaches for heart disease diagnosis.
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