ADVANCED MACHINE LEARNING MODELS FOR EARLY DETECTION OF CARDIAC DISORDERS

Abstract

Cardiovascular diseases (CVDs) are among the leading causes of mortality worldwide, making early detection a critical aspect of preventive healthcare. Traditional diagnostic techniques often rely on clinical expertise and invasive tests, which may delay timely intervention. Recent advancements in machine learning (ML) provide powerful tools for analyzing complex medical datasets, enabling predictive modeling and early risk assessment. This paper presents an overview of advanced machine learning models applied for the early detection of cardiac disorders, focusing on their predictive accuracy, computational efficiency, and applicability in real-world healthcare settings. Various supervised and ensemble learning algorithms, including Support Vector Machines (SVM), Random Forest (RF), Gradient Boosting, and Deep Neural Networks (DNN), are explored. A comparative study highlights the effectiveness of hybrid and ensemble approaches in achieving higher precision and recall rates. Experimental validation using benchmark datasets demonstrates that ML-based prediction significantly improves diagnosis accuracy while reducing false alarms. The study concludes that machine learning has the potential to revolutionize cardiac healthcare by enabling early detection, personalized treatment, and real-time monitoring through integration with wearable devices.