Multimodal Deep Learning for Structural Heart Disease Prediction from ECG and Clinical Data
Ajadi, N. A.; Afolabi, S. O.; Adenekan, I. O.; Jimoh, A. O.; Ajayi, A. O.; Adeniran, T. A.; Adepoju, G. D.; Hassan, N. F.; Ajadi, S. A.
Show abstract
This research presents multimodal deep learning for structural heart disease prediction. We evaluated multiple deep learning architectures, including TCN, Simple CNN, ResNet1d18, Light transformer and Hybrid model. The models were examined across the three seeds to ensure robustness, and bootstrap confidence interval is used to measure performance differences. TCN consistently outperforms other competing architectures, achieving statistically significant improvements with stable performance across runs. Similarly in predictive analysis, TCN has efficient computation and stable training compared to all competing architectures. Our results show that TCN emphasizes fairness evaluation when developing deep learning models for healthcare applications.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Improving Heart Disease Probability Prediction Sensitivity with a Grow Network Model 97%
- An algorithm to detect dicrotic notch in arterial blood pressure and photoplethysmography waveforms using the iterative envelope mean method 95%
- Digitizing ECG image: new fully automated method and open-source software code 94%
Similar papers in this journal
- A machine-learning Approach for Stress Detection Using Wearable Sensors in Free-living Environments 96%
- Detecting Heart Failure using novel bio-signals and a Knowledge Enhanced Neural Network 96%
- Identification of Myocardial Infarction (MI) Probability from Imbalanced Medical Survey Data: An Artificial Neural Network (ANN) with Explainable AI (XAI) Insights 96%
Similar papers in this journal
- Building Large-Scale Registries from Unstructured Clinical Notes using a Low-Resource Natural Language Processing Pipeline 94%
- Deep ensemble multitask classification of emergency medical call incidents combining multimodal data improves emergency medical dispatch 94%
- Stability of feature selection utilizing Graph Convolutional Neural Network and Layer-wise Relevance Propagation 94%
Similar papers in this journal
- HeartNet: Self Multi-Head Attention Mechanism via Convolutional Network with Adversarial Data Synthesis for ECG-based Arrhythmia Classification 98%
- A computationally efficient approach to segmentation of the aorta and coronary arteries using deep learning 96%
- SN-FPN: Self-attention Nested Feature Pyramid Network for Digital Pathology Image Segmentation 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.