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Explainable AI-driven intelligent system for precision forecasting in cardiovascular disease.

Anas Bilal1, Abdulkareem Alzahrani2, Khalid Almohammadi3

  • 1College of Information Science and Technology, Hainan Normal University, Haikou, China.

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|July 24, 2025
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Summary

This study introduces an Explainable Artificial Intelligence (XAI) system for predicting cardiovascular diseases (CVDs), enhancing trust and accuracy in healthcare. The XAI approach improves prediction reliability, aiding clinicians in patient care decisions.

Keywords:
cardiovascular diseaseselectronic medical recordsexplainable artificial intelligencelimemachine learningshap

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Area of Science:

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

Background:

  • Cardiovascular diseases (CVDs) pose a significant global health challenge, complicated by the difficulty of early, accurate prediction.
  • Traditional machine learning models for CVD prediction often function as "black boxes," limiting clinical trust and usability.
  • Explainable Artificial Intelligence (XAI) offers a potential solution by providing transparency into AI decision-making processes.

Purpose of the Study:

  • To introduce an intelligent forecasting system for cardiovascular events utilizing Explainable Artificial Intelligence (XAI).
  • To address the limitations of traditional, opaque machine learning models in predicting cardiovascular diseases.
  • To enhance the trustworthiness and usability of AI-driven predictions in clinical settings.

Main Methods:

  • Developed an intelligent forecasting system integrating advanced machine learning algorithms with XAI.
  • Utilized a comprehensive dataset of 308,737 patient records from Kaggle, including demographics, clinical measurements, and lifestyle factors.
  • Applied XAI techniques to provide understandable explanations for AI-driven cardiovascular event predictions.

Main Results:

  • The proposed XAI system achieved 91.94% accuracy in predicting cardiovascular events.
  • The system demonstrated a reduced miss rate of 8.06% compared to previous methods.
  • XAI integration enhanced the transparency and trustworthiness of AI predictions for healthcare professionals.

Conclusions:

  • Explainable Artificial Intelligence (XAI) significantly enhances the transparency and reliability of cardiovascular disease prediction.
  • The developed XAI system improves clinical decision-making, leading to better patient care and treatment delivery.
  • XAI holds substantial potential to advance cardiovascular healthcare through increased trust and usability of AI tools.