Naive Bayes and SVM-based Classification of Myocardial Tissue with Echocardiography Images for Heart Disease Diagnosis

Echocardiography is a non-invasive imaging technique for visualizing the heart and its functions. Accurate classification of myocardial tissue in echocardiography images is crucial for diagnosing and treating heart diseases. This research work suggests a new technique for categorizing cardiac tissue by utilizing the Naive Bayes (NB) and Support Vector Machine (SVM) algorithms. Preprocessing extracts texture, shape, and intensity-based features, followed by classification using NB and SVM. Evaluation metrics include accuracy, sensitivity, specificity, and precision. The results demonstrate high accuracy, with NB slightly outperforming SVM. The proposed method offers an efficient and accurate approach for clinical myocardial tissue classification, benefiting heart disease diagnosis and treatment.