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XAI-Augmented Voting Ensemble Models for Heart Disease Prediction: A SHAP and LIME-Based Approach.

Nermeen Gamal Rezk1, Samah Alshathri2, Amged Sayed3,4

  • 1Department of Computer Science and Engineering, Faculty of Engineering, Kafrelsheikh University, Kafr_El_Sheikh 6860404, Egypt.

Bioengineering (Basel, Switzerland)
|October 25, 2024
PubMed
Summary

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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
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This study introduces Explainable artificial intelligence (XAI) to improve heart disease prediction models. By combining ensemble learning with XAI techniques, the research enhances model transparency and accuracy for better healthcare decisions.

Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Cardiovascular Disease Research

Background:

  • Ensemble Learning (EL) models have been used for heart disease classification for nearly a decade.
  • Existing non-interpretable "black box" models present challenges in understanding their internal decision-making processes for heart disease prediction.
  • Accurate heart disease prediction remains a significant challenge in healthcare, impacting timely diagnosis and treatment.

Purpose of the Study:

  • To develop a framework for heart disease prediction using Explainable artificial intelligence (XAI)-based hybrid Ensemble Learning (EL) models.
  • To enhance the interpretability of predictive models through SHAP (SHapley Additive explanations) and LIME (Local Interpretable Model-agnostic explanations) analysis.
  • To identify the optimal hybrid ensemble learning algorithm for accurate heart disease prediction (HDP).
Keywords:
LIME (Local Interpretable Model-agnosticExplanations)SHAP (SHapley Additive exPlanations)explainable artificial intelligence (XAI)heart disease predictionhybrid Ensemble learningvoting algorithms

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Main Methods:

  • Implementation of a framework combining hybrid Ensemble Learning (EL) algorithms (LightBoost, XGBoost) with XAI techniques.
  • Application of SHAP and LIME for analyzing and interpreting the behavior of the EL models.
  • Evaluation of model efficacy using key performance metrics including accuracy, precision, and recall.

Main Results:

  • The developed framework successfully integrated XAI with hybrid EL models for heart disease prediction.
  • SHAP and LIME analyses provided insights into the important factors and risk signals contributing to heart disease.
  • The study identified specific hybrid EL models demonstrating superior performance in HDP based on accuracy, precision, and recall.

Conclusions:

  • The integration of XAI with hybrid Ensemble Learning significantly improves the interpretability and transparency of heart disease prediction models.
  • Explainable AI is crucial for understanding the underlying factors in cardiovascular disease prediction, enhancing trust and clinical utility.
  • The study recommends incorporating XAI into healthcare models to support medical decision-making and improve patient outcomes.