Table 18 Comparison of the proposed system outcome with previous researches for Cleveland dataset
From: A hybrid cost-sensitive ensemble for heart disease prediction
Author | Method | Recall (%) | Specificity (%) |
|---|---|---|---|
Present study | Ensemble classifier | 89.68 | 89.31 |
Kahramanli and Allahverdi [63] | Hybrid neural network | 93 | 78.5 |
Shah et al. [64] | PPCA\(^{1}\) + SVM | 75 | 90.57 |
Marian and Filip [65] | Fuzzy rule-based classification | 84.70 | 92.90 |
Ali et al. [56] | Gaussian Naive Bayes classifier | 87.80 | 97.95 |
Ali et al. [57] | Deep neural network | 85.36 | 100 |
Ali et al. [58] | Hybrid SVM | 82.92 | 100 |
Ali et al. [59] | Deep belief network | 96.03 | 93.15 |
Arabasadi et al. [66] | Hybrid neural network-genetic algorithm | 88 | 91 |
Mokeddem and Ahmed [47] | Fuzzy classification model | 87.39 | 94.38 |
Bashir et al. [26] | Ensemble model | 73.68 | 92.86 |
Leema et al. [67] | Differential Evolution + BPNN\(^{2}\) | 82.35 | 92.31 |
Mokeddem and Atmani [68] | Decision Tree + Fuzzy Inference System | 92.44 | 96.18 |