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CardioRiskNet: A Hybrid AI-Based Model for Explainable Risk Prediction and Prognosis in Cardiovascular Disease.

Fatma M Talaat^1,2, Ahmed R Elnaggar³, Warda M Shaban⁴

¹Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh 33516, Egypt.

Bioengineering (Basel, Switzerland)

|August 29, 2024

Summary

This summary is machine-generated.

CardioRiskNet, an AI model, accurately predicts cardiovascular disease (CVD) risk using active learning and attention mechanisms. This advanced tool surpasses traditional methods, offering improved patient care and disease management.

Keywords:

active learning cardiovascular diseases (CVDs)eXplainable artificial intelligence risk prediction

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Area of Science:

Artificial Intelligence in Medicine
Cardiovascular Disease Research
Machine Learning for Healthcare

Background:

Cardiovascular diseases (CVDs) are a leading cause of death globally, necessitating improved risk assessment.
Traditional CVD risk assessment methods have limitations in precision and adaptability.
There is a need for advanced strategies to overcome the shortcomings of conventional risk prediction models.

Purpose of the Study:

To introduce CardioRiskNet, a hybrid AI-based model for enhanced cardiovascular disease risk assessment and prognostication.
To address the limitations of traditional CVD risk prediction methods.
To develop a transparent and accurate AI tool for healthcare professionals.

Main Methods:

CardioRiskNet integrates data preprocessing, feature selection, eXplainable AI (XAI), active learning, and attention mechanisms.
The model employs active learning for iterative sample selection and attention mechanisms for dynamic feature focus.
XAI integration ensures interpretability and transparency in the risk prediction process.

Main Results:

CardioRiskNet achieved superior performance with 98.7% accuracy, 98.7% sensitivity, 99% specificity, and 98.7% F1-Score.
Experimental results demonstrate the model's capability to accurately assess and prognosticate CVD risk.
The AI model significantly outperforms conventional risk assessment methods.

Conclusions:

CardioRiskNet offers a novel and high-performing approach to cardiovascular disease risk management.
The study highlights the potential of active learning and AI in advancing CVD prognostication.
CardioRiskNet provides a powerful tool for healthcare professionals, improving patient care and disease management.