Optimized Deep Learning Framework for Early Detection of Heart Disease

Abstract

Heart disease is one of the top causes of death worldwide, highlighting the importance of reliable and early diagnostic technologies. This paper provides an optimal deep learning architecture for early diagnosis of cardiac disease, with the goal of improving diagnostic accuracy and efficiency. Our approach uses convolutional neural networks (CNN) and advanced data preparation techniques to analyze crucial patient metrics such as electrocardiogram (ECG) signals, blood pressure, cholesterol levels, and other clinical markers. The model detects cardiac illness with high accuracy, sensitivity, and specificity after rigorous experimentation and optimization, which included hyperparameter tuning and feature selection. The framework is tested on a large dataset, with the findings confirming its robustness and suitability for real-world applications. The suggested deep learning model surpasses previous methods, making it a scalable and effective solution for early detection. This study contributes to the development of automated systems that help healthcare practitioners make timely, data-driven decisions, with the ultimate goal of lowering heart disease morbidity and mortality.