Coronary heart disease prediction models using machine learning and deep learning algorithms.
In: AIP Conference Proceedings, Jg. 2838 (2024-02-23), Heft 1, S. 1-8
Konferenz
Zugriff:
Coronary heart disease (CHD), colloquially referred to as cardiovascular disease (CVD), is the leading cause of death worldwide. Each individual may experience different symptoms or none at all, which means they may be unaware they have CHD until they suffer from chest pain, a heart attack, or cardiac arrest. These circumstances may be averted if we can forecast the early diagnosis of heart disease and identify the disease's major risk factors. Currently, prediction accuracy is insufficient, and the most essential risk factors remain mysterious. This article addresses numerous risk factors for coronary heart disease and gives prediction models for the disease utilizing supervised machine learning algorithms, namely the Random Forest and XGBoost algorithms, as well as the Artificial Neural Network (ANN), a deep-learning-based method. It makes use of a publicly available dataset of coronary heart disease patients from the Cleveland database of the UCI. The methodology consists of the preprocessing steps, feature engineering which involves the correlations between features, data splitting with a ratio of 70:30, and model training. The trained models are further optimized using the Grid Search optimization algorithm. The parameters of the Random Forest model are the number of trees = 1000, the maximum depth of the tree = None, and the minimum samples split = 2. The parameters of the XGBoost model are learning rate = 0.5, maximum depth of the tree = 10, and the number of trees = 200. The ANN is built using ReLU activation function, 5 layers consisting of 26 nodes, a learning rate of 0.001, and 378 parameters in total, as well as an epoch of 50. The models are evaluated using accuracy, precision, recall, and F-measure numbers. The results show that the Random Forest, XGBoost, and ANN algorithms have accuracies of 81.11%, 82.22%, and 86.67%, respectively. Equally important, the results of the feature importance signify the importance of maximum heart rate and nuclear stress test in predicting the early diagnosis of the disease. [ABSTRACT FROM AUTHOR]
Titel: |
Coronary heart disease prediction models using machine learning and deep learning algorithms.
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Autor/in / Beteiligte Person: | Bernand, Charles ; Mirand, Eka ; Aryun, Mediana |
Zeitschrift: | AIP Conference Proceedings, Jg. 2838 (2024-02-23), Heft 1, S. 1-8 |
Quelle: | 2024, Vol. 2838 Issue 1, p1-8. 8p.; Jg. 2838 (2024-02-23) 1, S. 1-8 |
Veröffentlichung: | 2024 |
Medientyp: | Konferenz |
ISSN: | 0094-243X (print) |
DOI: | 10.1063/5.0179929 |
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