TY  - JOUR
AU  - Priyadarsini, Indira 
AU  - Rao, I.S. Siva 
AU  - Swetha, P. 
AU  - Anuradha, T. 
AU  - Sujatha, V. 
AU  - Divya, B. 
AU  - Kumar, Konduru Kranthi 
PY  - 2024
TI  - A Novel Optimized Machine Learning Approach for Early Prediction of Heart Disease Using Bio-Inspired Algorithms
JF  - Journal of Computer Science
VL  - 21
IS  - 1
DO  - 10.3844/jcssp.2025.71.77
UR  - https://thescipub.com/abstract/jcssp.2025.71.77
AB  - This study presents a novel approach for early prediction of heart disease using bio-inspired optimization algorithms to select and optimize features. The proposed approach utilizes a Genetic Algorithm (GA), Bat Algorithm (BA), Bee Algorithm (BA), and Ant Colony Optimization (ACO) to optimize a set of four features for improved prediction accuracy. The optimization process aims to identify the most relevant features from a large feature set, enhancing the performance of the machine learning model used for prediction. Experimental results on benchmark heart disease datasets demonstrate the effectiveness of the proposed approach, achieving significant improvements in prediction accuracy compared to traditional feature selection methods. The findings highlight the potential of bio-inspired optimization algorithms in enhancing the predictive capabilities of machine learning models for early detection of heart disease.