ArticleScientific reports2025
Prediction of cardiovascular disease based on multiple feature selection and improved PSO-XGBoost model.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
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- A Hybrid Swin Transformer and Texture Feature Framework for Histopathological Classification of Paratuberculosis.Bioengineering (Basel, Switzerland) · 2026Article
- A comparative study of quantum-inspired PSO and EA with their binary variants for heart disease classification.Scientific reports · 2026Article
- Development of AI based behavioral feature patterns on influencing asymptomatic cardiovascular disease attributes: a dataset standardization approach.Scientific reports · 2026Article
- Machine learning and SHAP values for predicting coronary artery disease risk in Xinjiang, China.European journal of medical research · 2026Article
- Modeling the relationship between residents' happiness and human settlement quality: An IGSA-MLPNN-GARSON approach.PloS one · 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
- Machine learning approach for predicting the severity risk of obstructive sleep apnea syndrome.Frontiers in big data · 2026Article
- A Low-Cost IoT Sensor and Preliminary Machine-Learning Feasibility Study for Monitoring In-Cabin Air Quality: A Pilot Case from Almaty.Sensors (Basel, Switzerland) · 2025Article
- Mining the risk: early cardiovascular detection in workers.Frontiers in medicine · 2025Article
- Heart disease prediction using hybrid TabNet architecture with stacked ensemble learning.Frontiers in physiology · 2025Article
- Explainable XGBoost model and nomogram for risk factor identification and risk prediction in cerebral small vessel disease: a machine learning-based retrospective cohort study.Frontiers in neurologyArticle
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Authors and funding
7 authors.
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Abstract
Cardiovascular disease is a common disease that threatens human health. In order to predict it more accurately, this paper proposes a cardiovascular disease prediction model that combines multiple feature selection, improved particle swarm optimization algorithm, and extreme gradient boosting tree. Firstly, the dataset is preprocessed, and an XGBoost cardiovascular disease prediction model is constructed for model training and compare it with other algorithms. Then, combined with two factor Pearson correlation analysis and feature importance ranking, multiple feature selection is performed, with the optimal feature subset as the feature input. Finally, the improved particle swarm optimization algorithm is used to adjust the hyperparameters of the extreme gradient boosting tree algorithm, and selecting the optimal hyperparameter combination to construct the MFS-DLPSO-XGBoost model. The recall, precision, accuracy, F1 score, and area under the ROC curve (AUC) of the MFS-DLPSO-XGBoost model reached 71.4%, 76.3%, 74.7%, 73.6%, and 80.8%, respectively, which increased by 3.6%, 3.2%, 2.7%, 3.2%, and 2.3% compared to XGBoost. The results indicate that the model proposed in this article has good classification performance and can provide assistance for doctors and patients in predicting and preventing heart disease.
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