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Interpretable machine learning model for cardiovascular disease risk prediction: a feature decomposition-based study
Liliang Yu1, Jiancheng Wu1, Xin Wu2
1Chongqing Three Gorges Medical College, Chongqing, China.
Insights
Machine learning accurately predicts cardiovascular disease (CVD) risk. A novel deep learning model identified key risk factors like blood pressure and cholesterol, aiding early intervention.
Area of Science:
- Computational biology
- Medical informatics
- Machine learning in healthcare
Background:
- Cardiovascular disease (CVD) poses a significant global health challenge.
- Early prediction and identification of CVD risk factors are crucial for prevention.
- Machine learning (ML) offers promising tools for developing predictive models.
Purpose of the Study:
- To construct and validate machine learning models for predicting cardiovascular disease (CVD) risk.
- To evaluate the performance of a novel feature decomposition-based deep learning (FDDL) model.
- To identify key predictors of CVD using model interpretability techniques.
Main Methods:
- Utilized a large dataset of 68,205 respondents from Kaggle.
- Developed and tested a feature decomposition-based deep learning (FDDL) model.
- Compared FDDL against six other ML models and employed SHAP for interpretation.
Main Results:
- The FDDL model achieved high predictive performance: 75.52% accuracy, 78.14% precision, 71.68% recall, F1 score of 0.7522, and AUC-ROC of 0.7643.
- Diastolic blood pressure, cholesterol, systolic blood pressure, and age were identified as critical predictors.
- The Logistic Regression (LR) model showed the weakest performance.
Conclusions:
- An effective ML model for CVD risk prediction was developed.
- The model can assist clinicians in identifying high-risk individuals.
- Provides a basis for personalized preventive healthcare strategies for cardiovascular disease.
Background:
Cardiovascular 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).
Methods:
This 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.
Results:
The FDDL model demonstrated superior predictive capability, achieving benchmark metrics of 75.52% accuracy, 78.14% precision, 71.68% recall, an F1 score of 0.7522, and an AUC-ROC value of 0.7643. In contrast, the LR model exhibited the weakest predictive ability among the compared methods. SHAP value-based feature importance ranking identified diastolic blood pressure, cholesterol level, systolic blood pressure, and age as the most critical predictors for cardiovascular disease risk assessment in our dataset.
Conclusion:
We 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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