ArticleBMC public health2025
Interpretable machine learning model for cardiovascular disease risk prediction: a feature decomposition-based study.
Article in BMC public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Comorbidity indices in observational studies on cardiovascular risk.Open heart · 2026Observational
- PSO-BiLSTM-Attention: An Interpretable Deep Learning Model Optimized by Particle Swarm Optimization for Accurate Ischemic Heart Disease Incidence Forecasting.Bioengineering (Basel, Switzerland) · 2025Article
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Authors and funding
7 authors.
Funding
Abstract
backgroundCardiovascular disease (CVD) is a leading public health issue worldwide. The key to preventing CVD is the early prediction and identification of CVD risk factors. The aim of this study is to construct and validate CVD prediction models using machine learning (ML).
methodsThis study utilized 11 features from 68,205 CVD respondents in the Kaggle dataset. Experiments were conducted using a feature decomposition-based deep learning model (FDDL) to predict CVD incidence in this dataset. The proposed model was compared with six other machine learning models. Moreover, the SHAP method was employed to interpret the model in this study.
resultsThe FDDL model demonstrated superior predictive capability, achieving benchmark metrics of 75.52% accuracy, 78.14% precision, 71.68% recall, an F
conclusionWe have developed an ML model for predicting the risk of CVDs. This model shows potential to assist clinicians in identifying high-risk patients and providing a theoretical basis for personalized preventive healthcare measures.
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