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Related Experiment Video

Updated: Jan 10, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

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Heart disease prediction using hybrid TabNet architecture with stacked ensemble learning.

Rizwana Yasmeen1, Lal Khan2, Ahyoung Choi2

  • 1Department of Computer Science, National University of Modern Languages (NUML), Islamabad, Pakistan.

Frontiers in Physiology
|November 21, 2025
PubMed
Summary

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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This study introduces a novel ensemble model combining deep learning and tree-based methods for improved cardiovascular disease (CVD) risk prediction. The new framework enhances early detection accuracy, aiding clinical decision-making.

Area of Science:

  • Computational biology
  • Medical informatics
  • Machine learning in healthcare

Background:

  • Cardiovascular diseases (CVDs) are a leading global cause of mortality.
  • Current CVD prediction tools face limitations due to data noise and modest accuracy.
  • Early detection of CVDs is crucial for effective intervention and patient outcomes.

Purpose of the Study:

  • To develop an advanced stacked ensemble framework for enhanced cardiovascular disease risk prediction.
  • To integrate deep learning (TabNet) and tree-based (XGBoost) models for improved accuracy and interpretability.
  • To provide clinicians with a more reliable tool for early CVD detection.

Main Methods:

  • A stacked ensemble framework was developed, integrating TabNet and XGBoost.
Keywords:
TabNetXGBoostcardiovascular riskclinical decision supportensemble learningheart diseasemachine learning

Related Experiment Videos

Last Updated: Jan 10, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.3K
  • Logistic Regression (LR) or Support Vector Machine (SVM) was employed as a meta-learner.
  • The model was evaluated on Kaggle and UCI CVD datasets.

    • Main Results:

    • The proposed ensemble model demonstrated superior performance over baseline models.
    • Key performance metrics including accuracy, F1-score, precision, recall, ROC-AUC, PR-AUC, and MCC were significantly improved.
    • The framework successfully balanced predictive accuracy with model interpretability.

    Conclusions:

    • Combining deep learning and tree-based models offers a practical advancement in CVD risk prediction.
    • The developed ensemble framework supports clinicians in making more reliable decisions for early CVD detection.
    • This approach holds significant potential for improving patient outcomes in cardiovascular health.