ArticleSensors (Basel, Switzerland)2023
Efficient Data-Driven Machine Learning Models for Cardiovascular Diseases Risk Prediction.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed, 96 citations in OpenAlex.
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- A comparative performance analysis of ensemble learning and regularized neural networks in cardiovascular risk prediction.Frontiers in medical technology · 2026Article
- Do nutritional variables improve cardiovascular disease prediction? A comparative machine learning analysis.Frontiers in nutrition · 2026Article
- Heart failure risk prediction based on machine learning and interpretability analysis.Frontiers in medicine · 2026Article
- Quantitative Microbial Risk Assessment ofToxics · 2025Article
- Predicting incident cardio-metabolic disease among persons with and without depressive and anxiety disorders: a machine learning approach.Social psychiatry and psychiatric epidemiology · 2025Article
- The application of machine learning models in a resource-constrained environment.Irish journal of medical science · 2025Article
- Predicting cardiovascular risk with hybrid ensemble learning and explainable AI.Scientific reports · 2025Article
- Artificial Intelligence in Aquatic Biodiversity Research: A PRISMA-Based Systematic Review.Biology · 2025Review
- Optimizing Stroke Risk Prediction: A Primary Dataset-Driven Ensemble Classifier With Explainable Artificial Intelligence.Health science reports · 2025Article
- The Heart of Transformation: Exploring Artificial Intelligence in Cardiovascular Disease.Biomedicines · 2025Review
- Explainable AI-driven intelligent system for precision forecasting in cardiovascular disease.Frontiers in medicine · 2025Article
- Machine learning techniques for improved prediction of cardiovascular diseases using integrated healthcare data.Frontiers in artificial intelligence · 2025Article
- Web application using machine learning to predict cardiovascular disease and hypertension in mine workers.Scientific reports · 2024Article
- Mitigating Algorithmic Bias in AI-Driven Cardiovascular Imaging for Fairer Diagnostics.Diagnostics (Basel, Switzerland) · 2024Article
- Machine learning-based classification of valvular heart disease using cardiovascular risk factors.Scientific reports · 2024Article
- Article
- Pitfalls in Developing Machine Learning Models for Predicting Cardiovascular Diseases: Challenge and Solutions.Journal of medical Internet research · 2024Article
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Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cardiovascular diseases (CVDs) are now the leading cause of death, as the quality of life and human habits have changed significantly. CVDs are accompanied by various complications, including all pathological changes involving the heart and/or blood vessels. The list of pathological changes includes hypertension, coronary heart disease, heart failure, angina, myocardial infarction and stroke. Hence, prevention and early diagnosis could limit the onset or progression of the disease. Nowadays, machine learning (ML) techniques have gained a significant role in disease prediction and are an essential tool in medicine. In this study, a supervised ML-based methodology is presented through which we aim to design efficient prediction models for CVD manifestation, highlighting the SMOTE technique's superiority. Detailed analysis and understanding of risk factors are shown to explore their importance and contribution to CVD prediction. These factors are fed as input features to a plethora of ML models, which are trained and tested to identify the most appropriate for our objective under a binary classification problem with a uniform class probability distribution. Various ML models were evaluated after the use or non-use of Synthetic Minority Oversampling Technique (SMOTE), and comparing them in terms of Accuracy, Recall, Precision and an Area Under the Curve (AUC). The experiment results showed that the Stacking ensemble model after SMOTE with 10-fold cross-validation prevailed over the other ones achieving an Accuracy of 87.8%, Recall of 88.3%, Precision of 88% and an AUC equal to 98.2%.
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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 Socratic graph.