ArticleScientific reports2023
Analyzing the impact of feature selection methods on machine learning algorithms for heart disease prediction.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.
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Who cites it
25 citing papers in PubMed, 2 syntheses or guidelines pooled it, 119 citations in OpenAlex.
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- A comparative study of quantum-inspired PSO and EA with their binary variants for heart disease classification.Scientific reports · 2026Article
- A hybrid feature selection framework combining Artificial Bee Colony and decision trees for CVD risk assessment.Scientific reports · 2026Article
- An explainable AI-driven hybrid feature selection approach for coronary artery disease diagnosis.Scientific reports · 2026Article
- Assessment of gait speed in older adults using a short-distance walking test with smartphone motion sensors and machine learning.Scientific reports · 2025Article
- RAVE-HD: A Novel Sequential Deep Learning Approach for Heart Disease Risk Prediction in e-Healthcare.Diagnostics (Basel, Switzerland) · 2025Article
- Automated feature learning and survival prognostication in grade 4 glioma using supervised machine learning models.Journal of neuro-oncology · 2025Article
- Predicting the risk of threatened abortion using machine learning methods: a comparative study.BMC pregnancy and childbirth · 2025Article
- Comparison of machine learning models for mucopolysaccharidosis early diagnosis using UAE medical records.Scientific reports · 2025Article
- The Effect of Data Leakage and Feature Selection on Machine Learning Performance for Early Parkinson's Disease Detection.Bioengineering (Basel, Switzerland) · 2025Article
- Stacked Ensemble Learning for Classification of Parkinson's Disease Using Telemonitoring Vocal Features.Diagnostics (Basel, Switzerland) · 2025Article
- SGO enhanced random forest and extreme gradient boosting framework for heart disease prediction.Scientific reports · 2025Article
- Predicting coronary heart disease with advanced machine learning classifiers for improved cardiovascular risk assessment.Scientific reports · 2025Article
- A high-throughput experimentation platform for data-driven discovery in electrochemistry.Science advances · 2025Article
- Integrating multidimensional data analytics for precision diagnosis of chronic low back pain.Scientific reports · 2025Article
- Clinical Response Characteristics of Salivary Proteins in the Management Strategy of Diabetes-Associated Periodontitis.Journal of proteome research · 2025Article
- Exploration and comparison of the effectiveness of swarm intelligence algorithm in early identification of cardiovascular disease.Scientific reports · 2025Article
- Utilizing machine learning to predict hospital admissions for pediatric COVID-19 patients (PrepCOVID-Machine).Scientific reports · 2025Article
Corrections and comments
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Authors and funding
3 authors at 3 institutions in 1 country.
Funding
Abstract
The present study examines the role of feature selection methods in optimizing machine learning algorithms for predicting heart disease. The Cleveland Heart disease dataset with sixteen feature selection techniques in three categories of filter, wrapper, and evolutionary were used. Then seven algorithms Bayes net, Naïve Bayes (BN), multivariate linear model (MLM), Support Vector Machine (SVM), logit boost, j48, and Random Forest were applied to identify the best models for heart disease prediction. Precision, F-measure, Specificity, Accuracy, Sensitivity, ROC area, and PRC were measured to compare feature selection methods' effect on prediction algorithms. The results demonstrate that feature selection resulted in significant improvements in model performance in some methods (e.g., j48), whereas it led to a decrease in model performance in other models (e.g. MLP, RF). SVM-based filtering methods have a best-fit accuracy of 85.5. In fact, in a best-case scenario, filtering methods result in + 2.3 model accuracy. SVM-CFS/information gain/Symmetrical uncertainty methods have the highest improvement in this index. The filter feature selection methods with the highest number of features selected outperformed other methods in terms of models' ACC, Precision, and F-measures. However, wrapper-based and evolutionary algorithms improved models' performance from sensitivity and specificity points of view.
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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.