ArticleJournal of personalized medicine2023
Multi-Modal Stacking Ensemble for the Diagnosis of Cardiovascular Diseases.
Article in Journal of personalized medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 66 citations in OpenAlex.
- Trends in AI-based diagnosis and intervention of metabolic diseases: a bibliometric analysis of the literature from 2000 to 2024.Frontiers in medicine · 2025Pooled it
- Cardiac arrhythmia detection via PQRST analyzed data using an optimized hierarchical fused fuzzy deep reinforcement learning.BMC medical informatics and decision making · 2026Article
- Multimodal Transformer-Based Electrocardiogram Analysis for Cardiovascular Comorbidity Detection: Model Development and Validation Study.JMIR formative research · 2026Article
- TC check: a web app for thyroid cancer recurrence prediction using explainable machine learning.Journal of cancer research and clinical oncology · 2025Article
- Efficient pretraining of ECG scalogram images using masked autoencoders for cardiovascular disease diagnosis.Scientific reports · 2025Article
- CELM: An Ensemble Deep Learning Model for Early Cardiomegaly Diagnosis in Chest Radiography.Diagnostics (Basel, Switzerland) · 2025Article
- Multimodal AI-approach for the automatic screening of cardiovascular diseases based on nocturnal physiological signals.NPJ cardiovascular health · 2025Article
- Deep Multi-Modal Skin-Imaging-Based Information-Switching Network for Skin Lesion Recognition.Bioengineering (Basel, Switzerland) · 2025Article
- Arrhythmia classification based on multi-input convolutional neural network with attention mechanism.PloS one · 2025Article
- Enhancing myocardial infarction detection with vectorcardiography: fusion-based comparative analysis of machine learning methods.Frontiers in physiology · 2025Article
- A multimodal stacked ensemble model for cardiac output prediction utilizing cardiorespiratory interactions during general anesthesia.Scientific reports · 2024Article
- Establishment and validation of a prediction nomogram for heart failure risk in patients with acute myocardial infarction during hospitalization.BMC cardiovascular disorders · 2023Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
backgroundCardiovascular diseases (CVDs) are a leading cause of death worldwide. Deep learning methods have been widely used in the field of medical image analysis and have shown promising results in the diagnosis of CVDs.
methodsExperiments were performed on 12-lead electrocardiogram (ECG) databases collected by Chapman University and Shaoxing People's Hospital. The ECG signal of each lead was converted into a scalogram image and an ECG grayscale image and used to fine-tune the pretrained ResNet-50 model of each lead. The ResNet-50 model was used as a base learner for the stacking ensemble method. Logistic regression, support vector machine, random forest, and XGBoost were used as a meta learner by combining the predictions of the base learner. The study introduced a method called multi-modal stacking ensemble, which involves training a meta learner through a stacking ensemble that combines predictions from two modalities: scalogram images and ECG grayscale images.
resultsThe multi-modal stacking ensemble with a combination of ResNet-50 and logistic regression achieved an AUC of 0.995, an accuracy of 93.97%, a sensitivity of 0.940, a precision of 0.937, and an F1-score of 0.936, which are higher than those of LSTM, BiLSTM, individual base learners, simple averaging ensemble, and single-modal stacking ensemble methods.
conclusionThe proposed multi-modal stacking ensemble approach showed effectiveness for diagnosing CVDs.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.