Research Article
Prediction of Cardiovascular Disease on Self-Augmented Datasets of Heart Patients Using Multiple Machine Learning Models
Table 2
Prediction of cardiovascular attacks.
| Reference | Gender based | Dataset | Feature selection techniques | Classification models | Accuracy | Optimization |
| Khan et al. [37] | No | A synthetic dataset across minority data space | No technique | Support machine vector | 83.65% for RBF and 84.56% for linear | No optimization technique | Chen et al. [14] | No | 17 left ventricle defective patients | No technique | Machine learning basic models | 79% on an average | No optimization technique | Mehta et al. [16] | No | Self-created data | No technique | Machine learning basic models | 79% on an average | No optimization technique | Ahmad et al. [4] | No | Self-created data | No technique | Machine learning basic models | 79% on an average | No optimization technique | Zahid et al. [8] | No | Self-created data | No technique | Support machine vector | 72% | No optimization technique | Chicco and Jurman [6] | No | Self-created data | No technique | Support machine vector | 76% | No optimization technique |
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