Key result
Principal component analysis-support vector machine (PCA-SVM) via Radial Basis Function kernel achieved a best classification accuracy of 88.24% for heart-disease diagnosis.
Why the study?
Do support vector machine-based approaches (SVM-RFE and PCA-SVM) provide high classification accuracy for heart-disease diagnosis?
Do support vector machine-based approaches (SVM-RFE and PCA-SVM) provide high classification accuracy for heart-disease diagnosis?
PCA-SVM using an RBF kernel and 6 principal components achieves an 88.24% accuracy in diagnosing heart disease, demonstrating the potential of data-driven machine learning approaches for this application.
May aid diagnostic tool development in cardiology; leaves open clinical adoption pending prospective validation.
Heart-disease diagnosis is widely studied by researchers all over the world, since it is the primary cause of deaths. There exist many challenges in heart-disease diagnosis, such as huge amount of data, high data dimension, large noise interference, etc, which point to the suitability of using data-driven approaches. This paper presents two dimension-reduction methodologies based on support vector machine (SVM), to diagnose heart disease. The most relevant features for diagnosis are achieved by support vector machine-recursive feature elimination (SVM-RFE) method. Then principal component analysis-support machine (PCA-SVM) is also used for heart-disease diagnosis. The best classification accuracy 88.24% is obtained by PCA-SVM via Radial Basis Function (RBF) kernel using only 6 principal components.
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Yang et al. (2018) studied Heart disease. Principal component analysis-support vector machine (PCA-SVM) and SVM-RFE was evaluated on Classification accuracy. Principal component analysis-support vector machine (PCA-SVM) via Radial Basis Function kernel achieved a best classification accuracy of 88.24% for heart-disease diagnosis.
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