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