Effective heart disease prediction with Grey-wolf with Firefly algorithm-differential evolution (GF-DE) for feature...

Area of Science:

• Cardiovascular Health
• Artificial Intelligence in Medicine
• Machine Learning for Healthcare

Background:

• Heart disease is a leading cause of mortality worldwide.
• Early and accurate prediction is crucial for effective treatment and patient outcomes.
• Artificial Intelligence offers powerful tools for analyzing complex healthcare data.

Purpose of the Study:

• To develop an improved feature selection and classification approach for high-accuracy heart disease prediction.
• To enhance clinical decision-making through intelligent data analysis.
• To reduce heart disease-related mortality rates.

Main Methods:

• Employed Grey-wolf with Firefly algorithm for effective feature selection.
• Utilized Differential Evolution Algorithm for optimizing Artificial Neural Network (ANN) hyperparameters.
• Proposed the Grey Wolf Firefly with Differential Evolution (GF-DE) model for enhanced classification.

Main Results:

• The GF-DE model demonstrated high accuracy in predicting heart disease.
• Comparative analysis showed superior performance over existing methods on Cleveland and Statlog datasets.
• Statistical analysis confirmed the significance and efficiency of the proposed system.

Conclusions:

• The proposed GF-DE method is highly effective for accurate heart disease prediction.
• This AI-driven approach assists practitioners in making informed medical decisions.
• The study highlights the potential of advanced AI techniques in cardiovascular health management.