Research Article
Computer-Aided Diagnostics of Heart Disease Risk Prediction Using Boosting Support Vector Machine
Table 4
Performances of different methods on Cleveland datasets.
| Author | Method | Accuracy (%) |
| Mirza et al. [31] | RBFSVM | 87.114 | Amen et al. [32] | Logistics regression | 82 | Sajja et al. [33] | SVM | 92–94 | Waris & Koteeswaran [34] | Novel KNN | 93 | Gupta et al. [35] | Naive Bayes | 88.16 | Saini et al. [36] | Hybrid classifier with weighted voting (HCWV) | 82.54 | Abdeldjouad et al. [37] | GFS-logicboost-C | 94.17 | Motarwar et al. [38] | AdaBoost | 80.32 | Alotaibi [39] | Decision tree | 93.19 | Gupta et al. [40] | Ensemble of Naïve Bayes, AdaBoost, and boosted tree | 87.97 | Proposed method | Boosting SVM | 99.92 |
|
|