ArticleDigital health
Exploring feature selection and classification methods for predicting heart disease.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- Development of machine learning-based multi-task quantitative structure-activity relationship models for predicting toxicities in six human organ systems.Computational toxicology (Amsterdam, Netherlands) · 2026Article
- Artificial Intelligence-Enabled Electrocardiography in Practice: A State-of-the-Art Review.Korean circulation journal · 2026Review
- Prediction of clinical outcomes of ST-elevated myocardial infarction patients using atmospheric solids analysis probe mass spectrometry and machine learning.The Analyst · 2025Article
- Identifying clinical feature clusters toward predicting stroke in patients with asymptomatic carotid stenosis.International journal of data science and analytics · 2025Article
- Combating cardiovascular disease disparities: The potential role of artificial intelligence.American journal of preventive cardiology · 2025Article
- Identifying stigmatizing and positive/preferred language in obstetric clinical notes using natural language processing.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- SRADHO: statistical reduction approach with deep hyper optimization for disease classification using artificial intelligence.Scientific reports · 2025Article
- Automated Data Transformation and Feature Extraction for Oxygenation-Sensitive Cardiovascular Magnetic Resonance Images.Journal of cardiovascular translational research · 2024Article
- Use of Artificial Intelligence in Improving Outcomes in Heart Disease: A Scientific Statement From the American Heart Association.Circulation · 2024Review
- Performance discrepancy mitigation in heart disease prediction for multisensory inter-datasets.PeerJ. Computer science · 2024Article
- Analyzing the impact of feature selection methods on machine learning algorithms for heart disease prediction.Scientific reports · 2023Article
- IoT-Enabled Classification of Echocardiogram Images for Cardiovascular Disease Risk Prediction with Pre-Trained Recurrent Convolutional Neural Networks.Diagnostics (Basel, Switzerland) · 2023Article
- Optimal Feature Selection-Based Dental Caries Prediction Model Using Machine Learning for Decision Support System.Bioengineering (Basel, Switzerland) · 2023Article
- Ensemble Learning Based on Hybrid Deep Learning Model for Heart Disease Early Prediction.Diagnostics (Basel, Switzerland) · 2022Article
- Effective hybrid feature selection using different bootstrap enhances cancers classification performance.BioData mining · 2022Article
- Analysis of risk factors associated with acute respiratory infections among under-five children in Uganda.BMC public health · 2022Article
- Machine Learning-Based Diabetic Neuropathy and Previous Foot Ulceration Patients Detection Using Electromyography and Ground Reaction Forces during Gait.Sensors (Basel, Switzerland) · 2022Article
- Machine learning-based diagnosis and risk factor analysis of cardiocerebrovascular disease based on KNHANES.Scientific reports · 2022Article
- Machine Learning Technology-Based Heart Disease Detection Models.Journal of healthcare engineering · 2022Article
- Significance of Visible Non-Invasive Risk Attributes for the Initial Prediction of Heart Disease Using Different Machine Learning Techniques.Computational intelligence and neuroscience · 2022Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning has been used successfully to improve the accuracy of computer-aided diagnosis systems. This paper experimentally assesses the performance of models derived by machine learning techniques by using relevant features chosen by various feature-selection methods. Four commonly used heart disease datasets have been evaluated using principal component analysis, Chi squared testing, ReliefF and symmetrical uncertainty to create distinctive feature sets. Then, a variety of classification algorithms have been used to create models that are then compared to seek the optimal features combinations, to improve the correct prediction of heart conditions. We found the benefits of using feature selection vary depending on the machine learning technique used for the heart datasets we consider. However, the best model we created used a combination of Chi-squared feature selection with the BayesNet algorithm and achieved an accuracy of 85.00% on the considered datasets.
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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.