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HyCARD-Net: A Synergistic Hybrid Intelligence Framework for Cardiovascular Disease Diagnosis

Rajan Das Gupta, Xiaobin Wu, Xun Liu, Jiaqi He

TL;DR

The paper addresses the challenge of predicting cardiovascular disease across heterogeneous datasets by introducing a hybrid framework that fuses deep learning and traditional machine learning. It combines a CNN-LSTM backbone with KNN and XGB classifiers in an ensemble voting scheme to leverage complementary representations and decision boundaries. On two public Kaggle datasets, the approach achieves 82.30% and 97.10% accuracy, with improvements in precision, recall, and F1-score, demonstrating strong cross-dataset generalization. This work offers a robust, interpretable pathway for AI-assisted CVD diagnosis and aligns with UN SDG3 by supporting early detection and scalable healthcare solutions.

Abstract

Cardiovascular disease (CVD) remains the foremost cause of mortality worldwide, underscoring the urgent need for intelligent and data-driven diagnostic tools. Traditional predictive models often struggle to generalize across heterogeneous datasets and complex physiological patterns. To address this, we propose a hybrid ensemble framework that integrates deep learning architectures, Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM), with classical machine learning algorithms, including K-Nearest Neighbor (KNN) and Extreme Gradient Boosting (XGB), using an ensemble voting mechanism. This approach combines the representational power of deep networks with the interpretability and efficiency of traditional models. Experiments on two publicly available Kaggle datasets demonstrate that the proposed model achieves superior performance, reaching 82.30 percent accuracy on Dataset I and 97.10 percent on Dataset II, with consistent gains in precision, recall, and F1-score. These findings underscore the robustness and clinical potential of hybrid AI frameworks for predicting cardiovascular disease and facilitating early intervention. Furthermore, this study directly supports the United Nations Sustainable Development Goal 3 (Good Health and Well-being) by promoting early diagnosis, prevention, and management of non-communicable diseases through innovative, data-driven healthcare solutions.

HyCARD-Net: A Synergistic Hybrid Intelligence Framework for Cardiovascular Disease Diagnosis

TL;DR

The paper addresses the challenge of predicting cardiovascular disease across heterogeneous datasets by introducing a hybrid framework that fuses deep learning and traditional machine learning. It combines a CNN-LSTM backbone with KNN and XGB classifiers in an ensemble voting scheme to leverage complementary representations and decision boundaries. On two public Kaggle datasets, the approach achieves 82.30% and 97.10% accuracy, with improvements in precision, recall, and F1-score, demonstrating strong cross-dataset generalization. This work offers a robust, interpretable pathway for AI-assisted CVD diagnosis and aligns with UN SDG3 by supporting early detection and scalable healthcare solutions.

Abstract

Cardiovascular disease (CVD) remains the foremost cause of mortality worldwide, underscoring the urgent need for intelligent and data-driven diagnostic tools. Traditional predictive models often struggle to generalize across heterogeneous datasets and complex physiological patterns. To address this, we propose a hybrid ensemble framework that integrates deep learning architectures, Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM), with classical machine learning algorithms, including K-Nearest Neighbor (KNN) and Extreme Gradient Boosting (XGB), using an ensemble voting mechanism. This approach combines the representational power of deep networks with the interpretability and efficiency of traditional models. Experiments on two publicly available Kaggle datasets demonstrate that the proposed model achieves superior performance, reaching 82.30 percent accuracy on Dataset I and 97.10 percent on Dataset II, with consistent gains in precision, recall, and F1-score. These findings underscore the robustness and clinical potential of hybrid AI frameworks for predicting cardiovascular disease and facilitating early intervention. Furthermore, this study directly supports the United Nations Sustainable Development Goal 3 (Good Health and Well-being) by promoting early diagnosis, prevention, and management of non-communicable diseases through innovative, data-driven healthcare solutions.
Paper Structure (20 sections, 6 equations, 2 figures, 5 tables)

This paper contains 20 sections, 6 equations, 2 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Machine Learning Approaches
  4. Deep Learning Approaches
  5. Hybrid ML–DL Approaches
  6. Summary and Research Gap
  7. Proposed Model
  8. Model Rationale
  9. CVD-Specific Design Considerations
  10. Data Preprocessing
  11. Hybrid Architecture and Ensemble Strategy
  12. Computational Cost and Model Complexity
  13. Experiments and Results
  14. Datasets
  15. Evaluation Metrics
  16. ...and 5 more sections

Figures (2)

  • Figure 1: Overall architecture of the proposed hybrid CNN--LSTM--KNN--XGB framework for cardiovascular disease prediction.
  • Figure 2: Hybrid CNN–LSTM, KNN, and XGB Architecture for CVD Prediction