ANALYSIS OF A HARMONIZED OPTIMIZATION-DRIVEN CROSSOVER PERCEPTRON NETWORK FOR HEART DISEASE PREDICTION

Heart disease prediction requires accurate and efficient models capable of handling heterogeneous medical
data, mitigating noise distortions, and optimizing feature selection for enhanced classification performance.
To address these challenges, we propose the Harmonized Optimization-Driven Crossover Perceptron
Network (HOC-Perceptron), an integrated framework comprising Advanced Normalization-Noise Filtering
(ANNF) for robust data preprocessing, Greylag Goose Optimization (GGO) for feature selection, and
Crossover Arithmetic-Optimized Multi-Layer Perceptron (CAO-MLP) for classification. ANNF improves
signal quality by 27.8%, reducing noise-induced distortions while preserving diagnostic markers. GGO
enhances feature selection efficiency by 32.5%, ensuring optimal subset selection and reducing
computational complexity. CAO-MLP further boosts classification accuracy by 8.9%, achieving an overall
F1-score of 96.4%, outperforming baseline models in terms of convergence speed and generalizability. The
proposed HOC-Perceptron framework significantly enhances heart disease prediction reliability, offering a
computationally efficient and clinically interpretable solution for early diagnosis and risk assessment