Heart Disease Prediction Using Fast Track Gram Matrix Pca And Genitic Algorithm

Abstract

Heart disease is a leading cause of mortality on a global scale. Accurately predicting cardiovascular disease poses a significant challenge within clinical data analysis. The present study introduces a prediction model that utilizes various combinations of information and employs multiple established classification approaches. The proposed technique combines the genetic algorithm (GA) and the recursive feature elimination method (RFEM) to select relevant features, thus enhancing the model’s robustness. This project presents an intelligent system for heart disease prediction by integrating Fast Track Gram Matrix Principal Component Analysis (PCA) with Genetic Algorithm (GA) under a deep learning framework. The proposed approach optimizes feature selection and dimensionality reduction, enabling the model to learn significant patterns from medical datasets with high precision and speed. The Fast Track Gram Matrix PCA aids in reducing redundant features, while the Genetic Algorithm fine-tunes the learning process by selecting optimal feature subsets. Experimental results demonstrate superior accuracy and efficiency over traditional prediction models, establishing this hybrid approach as a powerful tool for early diagnosis of heart diseases.