Abstract

Cardiovascular diseases (CVDs) are a leading cause of mortality worldwide. Accurate and early diagnosis is critical for effective treatment. Traditional methods of medical imaging analysis often lack precision and efficiency. The challenge lies in enhancing the accuracy and efficiency of medical imaging analysis for CVD diagnosis using advanced computational methods. This study proposes a novel approach that integrates extreme learning machines (ELM) for feature extraction with neuro-fuzzy systems for classification. The ELMs efficiently extract relevant features from medical images, while the neuro-fuzzy systems classify these features with high accuracy. Experimental results demonstrate a significant improvement in diagnosis accuracy. The proposed method achieved a classification accuracy of 95.7%, sensitivity of 94.3%, and specificity of 96.2%. These results outperform several existing methods in terms of both accuracy and computational efficiency.