Abstract :

Cardiovascular Disease Continues To Be A Major Cause Of Death Globally. Early And Precise Predictions Are Necessary For Effective Preventive And Appropriate Medical Intervention. However, High Complexity And Redundancy Of Medical Data Often Disrupt The Efficiency Of ML Models Used To Predict Disease. Traditional ML Techniques Sometimes Encounter Difficulty In Identifying And Selecting The Most Important Features From Extensive Data Sets, Resulting In Reduced Predictive Accuracy And Increased Computational Complexity. Research Says That, We Propose A ML-based Frameworks Like Logistic Regression Extra Tree And Voting Classifier For Cardiovascular Disease Prediction That Incorporates Advanced Feature Selection Techniques To Improve 98% Of Accuracy. To Effectively Tackle This Challenge, The ANOVA Feature Selection Method Is Combined With Particle Swarm Optimization (PSO). Keywords: Anova FS, PSO, Logistic Regression, Extra Tree, Voting Classifier

Published:

18-11-2025

Issue:

Vol. 25 No. 11 (2025)

Page Nos:

128-135

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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