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Publications

Journal Article Epublish

PubMed: 40703259 DOI: 10.3389/fmed.2025.1596335

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

Front Med (Lausanne) 12:1596335

Authors: Anas Bilal, Abdulkareem Alzahrani, Khalid Almohammadi, Muhammad Saleem, Muhammad Sajid Farooq, Raheem Sarwar

Affiliations

  • College of Information Science and Technology, Hainan Normal University, Haikou, China.
  • Department of Computer Science, Faculty of Computing and Information, Al-Baha University, Al-Baha, Saudi Arabia.
  • Department of Computer Science, Applied College, University of Tabuk, Tabuk, Saudi Arabia.
  • Department of Computer Science, Air University, Islamabad, Pakistan.
  • Department of Cyber Security, NASTP Institute of Information Technology, Lahore, Pakistan.
  • OTEHM, Manchester Metropolitan University, Manchester, United Kingdom.

Abstract

Introduction

Cardiovascular diseases (CVDs) are complex and affect a large part of the world's population; early accurate and timely prediction is also complicated. Typically, predicting CVDs involves using statistical models and other forms of standard machine learning. Although these methods offer some level of prediction, their black-box nature severely hinders the ability of the healthcare professional to trust and use the predictions. The following are some of the challenges that Explainable Artificial Intelligence (XAI) may solve since it can give an understanding of the decision-making system of AI to build confidence and increase usability.

Methods

This research introduced an intelligent forecasting system for cardiovascular events using XAI and addressed the limitations of traditional methods. This proposed system incorporates advanced machine learning algorithms integrated with XAI to examine a dataset comprising 308,737 patient records with features including age, BMI, blood pressure, cholesterol levels, and lifestyle factors. This dataset was sourced from the Kaggle Cardiovascular Disease dataset.

Results

Incorporating XAI offers an understandable explanation so that the healthcare professional can understand and make the AI-driven prediction trustworthy enough to improve the decision-making of treatment and care delivery for the patients. The simulation results of the proposed system provide better results than those of the previously published research works in terms of 91.94% accuracy and 8.06% miss rate.

Discussion

This proposed system makes it clear that XAI has the potential to significantly improve cardiovascular healthcare by enhancing transparency, reliability, and the quality of patient care.

MeSH

Trust, Cardiovascular Diseases, Electronic Health Records, Machine Learning, Research, Learning, Artificial Intelligence, Electronic Medical Record, Intelligent Systems