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Comparison with existing machine learning models on benchmark Cleveland ischemic heart disease dataset
| Models | Accuracy |
|---|---|
| RBF kernel-based SVM14 | 82.18% |
| Fuzzy rule-based model15 | 84.00% |
| Logistic regression SVM16 | 84.85% |
| Hybrid random forest17 | 88.40% |
| Ensemble model18 | 90.00% |
| Neural network with hyper-parameter optimization19 | 90.78% |
| Our clinical knowledge–enhanced ML model | 94.40% |
ML = machine learning; RBF = radial basis function; SVM = support vector machine.
