A hybrid one-dimensional convolutional neural network achieved 76.9% accuracy for coronary heart disease and 80.1% for non-coronary heart disease, outperforming contemporary machine learning models.
Does a hybrid 1D CNN improve the accuracy of detecting cardiovascular disease from clinical parameters compared to traditional machine learning algorithms?
A hybrid 1D CNN using clinical parameters from online survey data demonstrates improved accuracy for detecting cardiovascular disease compared to traditional machine learning models.
Heart disease is a significant public health problem, and early detection is crucial for effective treatment and management. Conventional and noninvasive techniques are cumbersome, time-consuming, inconvenient, expensive, and unsuitable for frequent measurement or diagnosis. With the advance of artificial intelligence (AI), new invasive techniques emerging in research are detecting heart conditions using machine learning (ML) and deep learning (DL). Machine learning models have been used with the publicly available dataset from the internet about heart health; in contrast, deep learning techniques have recently been applied to analyze electrocardiograms (ECG) or similar vital data to detect heart diseases. Significant limitations of these datasets are their small size regarding the number of patients and features and the fact that many are imbalanced datasets. Furthermore, the trained models must be more reliable and accurate in medical settings. This study proposes a hybrid one-dimensional convolutional neural network (1D CNN), which uses a large dataset accumulated from online survey data and selected features using feature selection algorithms. The 1D CNN proved to show better accuracy compared to contemporary machine learning algorithms and artificial neural networks. The non-coronary heart disease (no-CHD) and CHD validation data showed an accuracy of 80.1% and 76.9%, respectively. The model was compared with an artificial neural network, random forest, AdaBoost, and a support vector machine. Overall, 1D CNN proved to show better performance in terms of accuracy, false negative rates, and false positive rates. Similar strategies were applied for four more heart conditions, and the analysis proved that using the hybrid 1D CNN produced better accuracy.
Mamun et al. (Mon,) conducted a other in Cardiovascular Disease. Hybrid one-dimensional convolutional neural network (1D CNN) vs. Artificial neural network, random forest, AdaBoost, and support vector machine was evaluated on Accuracy, false negative rates, and false positive rates. A hybrid one-dimensional convolutional neural network achieved 76.9% accuracy for coronary heart disease and 80.1% for non-coronary heart disease, outperforming contemporary machine learning models.
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