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Detecting cardiovascular disease early is crucial in healthcare, especially in the field of cardiology. With 12 million deaths worldwide annually, the disease is a significant health concern. Early detection can lead to lifestyle changes that reduce the risk of complications. Machine learning techniques can be used to extract useful data from large datasets and make accurate predictions, requiring less investment. Machine learning algorithms can also be used to address unbalanced datasets and feature selection, which can improve diagnostic accuracy. Using ensemble learning with a machine learning algorithm, feature selection, and biomedical test values can help classify cardiovascular disease. Multiple classifier models can also be applied to improve accuracy with an ensemble classifier. |
