A Reliable Machine Intelligence Model for Accurate Identification of Cardiovascular Diseases Using Ensemble...

Area of Science:

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

Background:

• Clinical data analysis is crucial for early cardiovascular disease detection.
• Existing artificial intelligence (AI) models show potential but have room for accuracy improvement.
• Optimizing model and feature combinations can enhance diagnostic and prognostic capabilities.

Purpose of the Study:

• To propose a reliable ensemble model for improved cardiovascular disease classification and forecasting.
• To enhance the accuracy of machine intelligence in processing clinical data for health predictions.

Main Methods:

• Development of a novel ensemble model integrating multiple machine learning algorithms.
• Feature selection and combination strategies to optimize predictive performance.
• Validation of the proposed model on diverse cardiovascular disease datasets.

Main Results:

• The ensemble model achieved 96.75% accuracy on the Mendeley Data Center dataset.
• Achieved 93.39% accuracy on the IEEE DataPort comprehensive dataset.
• Demonstrated 88.24% accuracy on the Cleveland heart disease dataset.

Conclusions:

• The proposed ensemble model significantly improves the accuracy of cardiovascular disease prediction.
• This advancement contributes to enhanced patient safety and health security through early intervention.
• The study highlights the potential of ensemble methods in machine intelligence for clinical decision support.