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Published on: September 20, 2024
A cardiovascular disease prediction method based on cross-combination strategy and dynamic weighted stacking ensemble
Xiaobo Qi1,2, Jingli Gao3, Hui Qi3,4
1School of Computer Science and Technology, Taiyuan Normal University, Jinzhong, 030619, China. xbqi@tynu.edu.cn.
Insights
This study introduces a new Cardiovascular Disease (CVD) prediction model using a dynamic weighted stacking ensemble. The novel method significantly improves prediction accuracy, aiding in early detection and reducing mortality rates.
Area of Science:
- Medical Informatics
- Machine Learning in Healthcare
- Computational Biology
Background:
- Cardiovascular disease (CVD) remains a leading global cause of mortality.
- Early and accurate CVD prediction is critical for effective intervention and mortality reduction.
- Existing models face challenges in feature association mining and generalization.
Purpose of the Study:
- To develop an advanced Cardiovascular Disease (CVD) prediction method.
- To address limitations in feature association and model generalization in current CVD prediction tools.
- To enhance the accuracy and reliability of CVD risk assessment.
Main Methods:
- Proposes a Cardiovascular Disease (CVD) prediction method using an ensemble of cross-combination strategy and dynamic weighted stacking ensemble (CCS-DWSE).
- Constructs heterogeneous base models via multi-feature selection and multi-paradigm classifiers.
- Employs a dynamic weighted stacking ensemble framework with real-time weight adjustment and a K-Nearest Neighbors (KNN) meta-model for adaptive fusion.
- Utilizes SHAP for feature interpretability analysis.
Main Results:
- Achieved high prediction accuracies of 98.32%, 92.33%, and 82.10% across three public datasets.
- Attained an Area Under the Curve (AUC) of up to 0.9992.
- Demonstrated a 2.76% performance improvement over existing state-of-the-art models.
Conclusions:
- The CCS-DWSE method offers an efficient and scalable solution for Cardiovascular Disease (CVD) prediction.
- Presents a novel approach for collaborative multi-model learning on complex medical data.
- Highlights the potential of dynamic ensemble methods for improving diagnostic accuracy in healthcare.
Abstract:
Cardiovascular disease (CVD) is one of the leading causes of death worldwide, early and accurate prediction is crucial for reducing both incidence and mortality rates. Aiming at the problems of insufficient feature association mining and poor generalisation ability of statically weighted ensemble in existing models, this paper proposes a CVD prediction method based on the ensemble of cross-combination strategy and dynamic weighted stacking ensemble (CCS-DWSE). The method firstly constructs a heterogeneous base model through a full combination of multi-feature selection techniques and multi-paradigm classifiers. Next, it designs a dynamic weighted stacking ensemble framework that adjusts the base model weights in real time, and adaptively fuses the prediction results of the K-Nearest Neighbors (KNN) meta-model. Finally, SHAP-based interpretability analysis is employed to quantify the contribution of each feature. The experimental results show that CCS-DWSE achieves accuracies of 98.32%, 92.33%, and 82.10% on three public datasets, with an area under the curve (AUC) of up to 0.9992, representing a 2.76% improvement over the best existing models. This study provides an efficient and scalable solution for CVD prediction and offers a novel perspective for collaborative multi-model learning on complex medical data.
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