Heart Risk Failure Prediction Using a Novel Feature Selection Method for Feature Refinement and Neural Network for Classification

Table 5

Classification accuracies of the proposed method and other methods in the literature that used heart disease dataset.

Study (year)	Method	Accuracy (%)
ToolDiag, RA [35]	IB1-4	50.00
WEKA, RA [35]	InductH	58.50
ToolDiag, RA [35]	RBF	60.00
WEKA, RA [35]	FOIL	64.00
ToolDiag, RA [35]	MLP + BP	65.60
WEKA, RA [35]	T2	68.10
WEKA, RA [35]	1R	71.40
WEKA, RA [35]	IB1c	74.00
WEKA, RA [35]		76.70
Robert Detrano [35]	Logistic regression	77.00
Paul et al. [19]	FDSS	80
Khemphila and Boonijing (2011) [36]	MLP-backpropagation	80.99
Newton Cheung (2001) [37]	BNNF	80.96
Newton Cheung (2001) [37]	C4.5	81.11
Newton Cheung (2001) [37]	Naïve Bayes	81.48
Newton Cheung (2001) [37]	BNND	81.11
WEKA, RA [35]	Naïve Bayes	83.60
Šter and Dobnikar [38]	Fisher discriminant analysis	84.2
Šter and Dobnikar [38]	Linear discriminant analysis	84.5
Šter and Dobnikar [38]	Naïve Bayes	82.5–83.4
Polat et al. (2005) [25]	AIRS	84.50
Dwivedi (2018) [22]	LR	85
Ozsen et al. (2005) [39]	Kernel functions with AIS	85.93
Kahramanli and Allahverdi (2008) [40]	Hybrid neural network system	86.8
Polat et al. (2006) [10]	Fuzzy-AIRS-knn based system	87.00
Amin et al.(2019) [23]	LR, SVM, K-NN	87.4
Verma et al. (2016) [20]	CFS-PSO	88.4
Das et al. (2009) [1]	Neural network ensembles	89.01
Jankowski and Kadirkamanathan (1997) [41]	IncNet	90.00
Ali and Bukhari (2019) [12]	Mutual information + DNN	90
Kumar (2011) [42]	ANFIS	91.18
Shah et al. (2017) [21]	PPCA	91.30
Samuel et al. (2017) [4]	ANN-fuzzy-AHP	91.10
Kumar (2012) [43]	Fuzzy resolution mechanism	91.83
Ali et al. (2019) [34]	Stacked SVMs	92.22
Proposed method (2020)	Feature selection based on FWAFE + ANN	91.11
Proposed method (2020)	Feature selection based on FWAFE + DNN	93.33