Research Article
A Reliable Machine Intelligence Model for Accurate Identification of Cardiovascular Diseases Using Ensemble Techniques
Table 4
Achieved accuracies using benchmark classifiers.
| Classification technique | Accuracy (%) achieved with the Cleveland dataset | Accuracy (%) achieved with the comprehensive dataset | Accuracy (%) achieved with the Mendeley dataset |
| Decision tree | 77.86 | 82.56 | 95 | Random forest | 78.68 | 90.75 | 95.12 | Naive Bayes | 81.14 | 84.24 | 94.25 | Logistic regression | 81.96 | 84.03 | 95.25 | Support vector machine | 79.05 | 81.52 | 93.15 | Gradient boosting | 81.14 | 86.13 | 95.15 | XGBoost | 80.32 | 88.23 | 96.12 |
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