Research Article
A Reliable Machine Intelligence Model for Accurate Identification of Cardiovascular Diseases Using Ensemble Techniques
Table 1
Existing models’ accuracy comparison.
| Authors | Model used | Accuracy (%) |
| AI-Milli [9] | NN | 81 | Sonawane and Patil [10] | MPNN | 98 | Dai et al. [11] | AdaBoost | 82 | Radhimeenakshi [13] | SVM, ANN | 86 | Saqlain et al. [14] | LR and RF | 80.69 | Karaylan and Kilic [17] | ANN | 95 | Esfahani and Ghazanfari [18] | DT | 86.80 | Cheng and Chiu [19] | ANN | 82.5 | Doppala et al. [23] | Hybrid model | 84.40 | Nasarian et al. [24] | Hybrid feature selection | 81.23 | Doppala et al. [26] | Ensemble | 85.24 | Kumar et al. [32] | CNN | 88 | Bayu Adhi et al. [29] | Ensemble | 93.55 | Doppala et al. [30] | GA-RBF | 85.40, 94.20 | Waqas Nadeem et al. [34] | SVM | 96.23 |
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