ArticleDiagnostics (Basel, Switzerland)2024
Machine Learning-Based Predictive Models for Detection of Cardiovascular Diseases.
Article in Diagnostics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
50 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Harnessing Machine Learning, a Subset of Artificial Intelligence, for Early Detection and Diagnosis of Type 1 Diabetes: A Systematic Review.International journal of molecular sciences · 2025Pooled it
- Review
- A Comparison of Machine Learning Algorithms for Predicting Hypertension Incidence Based on Cohort Study.Endocrinology, diabetes & metabolism · 2026Article
- Scaling trachoma surveillance in endemic areas using machine learning.BMC infectious diseases · 2026Article
- Article
- Contrast-Free Myocardial Infarction Segmentation with Attention U-Net.Diagnostics (Basel, Switzerland) · 2026Article
- Sparse Temporal AutoEncoder for ECG Anomaly Detection.Sensors (Basel, Switzerland) · 2026Article
- Decoding cardiovascular risk in Chinese middle-aged and elderly adults: a 9-year prospective study integrating machine learning with explainable AI based on CHARLS cohort.BMC medical informatics and decision making · 2026Article
- Article
- A hybrid deep learning and large language model framework for MACE risk prediction and evidence-based clinical recommendations.Frontiers in cardiovascular medicine · 2026Article
- Web-based cardiovascular disease risk prediction using machine learning.Frontiers in artificial intelligence · 2026Article
- Enhancing clinically cardiovascular machine learning model for risk prediction via sample augmentation.Frontiers in medicine · 2026Article
- An explainable machine learning framework for cardiovascular risk prediction using structured health data.Frontiers in artificial intelligence · 2026Article
- Comparative analysis of machine learning techniques for cardiovascular disease prediction.PloS one · 2026Article
- Nomogram analysis of factors associated with prognosis in patients with sepsis-associated acute kidney injury: a case-control study based on LASSO regression.Frontiers in medicine · 2026Article
- Deep learning for cardiovascular disease: a comprehensive review of detection and risk forecasting.Frontiers in artificial intelligence · 2026Review
- Unsupervised machine learning for cardiovascular disease: A framework for future studies.European journal of heart failure · 2025Review
- Automated heart disease detection using Swin Transformer and ECG signal processing: a high-accuracy approach.Scientific reports · 2025Article
- Using Sentinel 2A and Landsat 8 imagery to assess changes in forest carbon storage.Scientific reports · 2025Article
- Deep Learning for Early Detection of Cardiovascular Diseases From Medical Imaging.Health science reports · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Cardiovascular diseases present a significant global health challenge that emphasizes the critical need for developing accurate and more effective detection methods. Several studies have contributed valuable insights in this field, but it is still necessary to advance the predictive models and address the gaps in the existing detection approaches. For instance, some of the previous studies have not considered the challenge of imbalanced datasets, which can lead to biased predictions, especially when the datasets include minority classes. This study's primary focus is the early detection of heart diseases, particularly myocardial infarction, using machine learning techniques. It tackles the challenge of imbalanced datasets by conducting a comprehensive literature review to identify effective strategies. Seven machine learning and deep learning classifiers, including K-Nearest Neighbors, Support Vector Machine, Logistic Regression, Convolutional Neural Network, Gradient Boost, XGBoost, and Random Forest, were deployed to enhance the accuracy of heart disease predictions. The research explores different classifiers and their performance, providing valuable insights for developing robust prediction models for myocardial infarction. The study's outcomes emphasize the effectiveness of meticulously fine-tuning an XGBoost model for cardiovascular diseases. This optimization yields remarkable results: 98.50% accuracy, 99.14% precision, 98.29% recall, and a 98.71% F1 score. Such optimization significantly enhances the model's diagnostic accuracy for heart disease.
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.