FEATURE-OPTIMIZED MACHINE LEARNING APPROACH FOR EARLY DETECTION OF CARDIOVASCULAR DISEASE

Abstract

Cardiovascular disease (CVD) remains one of the leading causes of mortality worldwide, making early detection crucial for effective treatment and prevention. Traditional diagnostic methods often require extensive clinical evaluation and may be prone to delays. This study proposes a Feature-Optimized Machine Learning Approach to enhance the accuracy and efficiency of CVD prediction. The system utilizes advanced feature selection techniques to identify the most relevant clinical parameters, reducing computational complexity while improving model performance. Various machine learning algorithms, including Support Vector Machines (SVM), Random Forest, and Deep Neural Networks (DNN), are evaluated to determine the most effective model for early diagnosis. The integration of feature selection not only enhances predictive accuracy but also eliminates redundant data, ensuring faster and more reliable disease classification. Experimental results demonstrate that the optimized approach significantly improves classification metrics such as accuracy, precision, and recall, making it a valuable tool for early cardiovascular risk assessment in clinical settings.