Echocardiographic cardiac views classification using whale optimization and weighted support vector machine

Abstract

Aim: A significant medical diagnostic tool for monitoring cardiovascular health and function is 2D electrocardiograms. For computerized echocardiogram (echo) analysis, recognizing how this device performs is essential. This paper primarily focuses on detecting the transducer's viewpoint in cardiac echo videos using spatiotemporal data. It distinguishes between different viewpoints by monitoring the heart's function and rate throughout the cycle of heartbeats. Computer-aided diagnosis (CAD) examination sizes are the first steps toward computerized classification of cardiac imaging tests. Since clinical analysis frequently starts with automatic classification, the current view can enhance the detection of Cardiac Vascular Disease (CVD).

Methods: This research article uses a Machine Learning (ML) algorithm called the Integrated Metaheuristic Technique (IMT), which is the Whale Optimization Algorithm with Weighted Support Vector Machine (WOA-WSVM).

Results: The parameters in the classification are optimized with the assistance of WOA, and the echo is classified using WSVM. The WOA-WSVM classifies the images effectively and achieves an accuracy of 98.4%.

Conclusion: The numerical analysis states that the WOA-WSVM technique outperforms the existing state-of-the-art algorithms.

Keywords

Cardiac vascular disease / cardiac view / machine learning / classification / image processing / accuracy

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