ArticleScientific reports2024
Comprehensive evaluation and performance analysis of machine learning in heart disease prediction.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence for Cardiovascular Risk Prediction: An Umbrella Review of Applications and Translational Challenges.Vascular health and risk management · 2026Pooled it
- Cardiovascular disease prediction and mortality risk assessment using a QSAO‑optimized BP neural network.BMC medical informatics and decision making · 2026Article
- CardioMetaHybridOptimizer as a behaviorally adaptive multi-phase metaheuristic framework for interpretable cardiovascular disease diagnosis.BMC bioinformatics · 2026Article
- Importance of balanced datasets with feature selection and ensemble methods on heart disease classification using distinctive machine learning techniques: a comparative analysis.Scientific reports · 2026Article
- Advancing cardiovascular disease diagnosis with an interpretable and responsible AI framework.Scientific reports · 2026Article
- Heart disease prediction using rough neutrosophic sets and dual-attention neural networks: RNS-OptiDANet.Frontiers in artificial intelligence · 2026Article
- Comparative performance of machine learning and deep learning models for heart disease prediction in a small clinical dataset.American journal of cardiovascular disease · 2026Article
- Prioritization of patients at risk of heart attack using a novel full-objective ITARA based on Random Forest and Decision tree.Scientific reports · 2025Article
- Harris Hawks optimization based deep learning models for heart disease diagnosis.Scientific reports · 2025Article
- Unveiling the potential of artificial intelligence in revolutionizing disease diagnosis and prediction: a comprehensive review of machine learning and deep learning approaches.European journal of medical research · 2025Review
- Machine Learning-Based Immuno-Inflammatory Index Integrating Clinical Characteristics for Predicting Coronary Artery Plaque Rupture.Immunity, inflammation and disease · 2025Article
- A systematic review of machine learning in heart disease prediction.Turkish journal of biology = Turk biyoloji dergisi · 2025Review
- Heart disease prediction using hybrid TabNet architecture with stacked ensemble learning.Frontiers in physiology · 2025Article
- Ensemble learning approach with explainable AI for improved heart disease prediction.Frontiers in pharmacology · 2025Article
- A medical disease assisted diagnosis method based on lightweight fuzzy SZGWO-ELM neural network model.Scientific reports · 2024Article
- Machine Learning Constructed Based on Patient Plaque and Clinical Features for Predicting Stent Malapposition: A Retrospective Study.Clinical cardiology · 2024Article
- Genetic Algorithm-based Convolutional Neural Network Feature Engineering for Optimizing Coronary Heart Disease Prediction Performance.Healthcare informatics research · 2024Article
Corrections and comments
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7 authors.
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Abstract
Heart disease is a leading cause of mortality on a global scale. Accurately predicting cardiovascular disease poses a significant challenge within clinical data analysis. The present study introduces a prediction model that utilizes various combinations of information and employs multiple established classification approaches. The proposed technique combines the genetic algorithm (GA) and the recursive feature elimination method (RFEM) to select relevant features, thus enhancing the model's robustness. Techniques like the under sampling clustering oversampling method (USCOM) address the issue of data imbalance, thereby improving the model's predictive capabilities. The classification challenge employs a multilayer deep convolutional neural network (MLDCNN), trained using the adaptive elephant herd optimization method (AEHOM). The proposed machine learning-based heart disease prediction method (ML-HDPM) demonstrates outstanding performance across various crucial evaluation parameters, as indicated by its comprehensive assessment. During the training process, the ML-HDPM model exhibits a high level of performance, achieving an accuracy rate of 95.5% and a precision rate of 94.8%. The system's sensitivity (recall) performs with a high accuracy rate of 96.2%, while the F-score highlights its well-balanced performance, measuring 91.5%. It is worth noting that the specificity of ML-HDPM is recorded at a remarkable 89.7%. The findings underscore the potential of ML-HDPM to transform the prediction of heart disease and aid healthcare practitioners in providing precise diagnoses, exerting a substantial influence on patient care outcomes.
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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.