Abstract :

Heart Disease Is One Of The Leading Causes Of Mortality Worldwide, Making Early And Accurate Prediction Crucial For Effective Prevention And Treatment. Traditional Statistical And Machine Learning Approaches Often Struggle To Optimize Predictive Performance Due To The High Dimensionality And Complexity Of Biomedical Datasets. This Study Proposes A Bio-inspired Algorithm-based Approach For Heart Disease Prediction, Leveraging Nature-inspired Optimization Techniques Such As Genetic Algorithms (GA), Particle Swarm Optimization (PSO), And Artificial Bee Colony (ABC) To Enhance Feature Selection And Model Accuracy. The Proposed Framework First Preprocesses And Normalizes Patient Datasets, Then Applies Bio-inspired Algorithms To Identify The Most Significant Features Influencing Heart Disease Risk. Selected Features Are Subsequently Fed Into Classification Models, Including Support Vector Machines (SVM), Decision Trees, And Neural Networks, To Predict The Likelihood Of Heart Disease. Experimental Results Demonstrate That Bio-inspired Algorithms Improve Feature Selection Efficiency, Reduce Computational Complexity, And Enhance The Overall Accuracy, Sensitivity, And Specificity Of Heart Disease Prediction Models. This Approach Provides A Robust Decision-support Tool For Medical Professionals, Enabling Timely Diagnosis And Improved Patient Care.

Published:

28-10-2025

Issue:

Vol. 25 No. 10 (2025)

Page Nos:

175-180

Section:

Articles

License:

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

How to Cite