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- W3213308033 abstract "Heart disease patient's classification is one of the most important keys in cardiovascular disease diagnosis. Researchers used several data mining methods to support healthcare specialists in the disease's analysis. This research has studied diverse of supervised machine learning systems for heart disease data classification, Decision Tree (DT), Artificial Neural Networks (ANN) classifiers, Naïve Bayes (NB), and Support Vector Machine (SVM), and have been used over two datasets of heart disease archives from the UCI machine-learning source. Results showed that ANN, the networks that are motivated via biological neural networks classifier overtook the three other classifiers with highest accuracy rate. The remaining classifiers returned lower performance than ANN. Moreover, enhancement is essential as misclassification is costly, so further improvement is required." @default.
- W3213308033 created "2021-11-22" @default.
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- W3213308033 date "2021-09-29" @default.
- W3213308033 modified "2023-09-25" @default.
- W3213308033 title "Cardiovascular Diseases Classification Via Machine Learning Systems" @default.
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- W3213308033 doi "https://doi.org/10.1109/3ict53449.2021.9581384" @default.
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