Table 18.

Comparison of the proposed system outcome with previous researches for Cleveland dataset

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

The values listed in the table represent the average performance on ten folds

${}^1$ Probabilistic principal component analysis

${}^2$ Back propagation neural networks