Artificial intelligence for heart disease prediction and imputation of missing data in cardiovascular datasets

Figures & data

Figure 1. Proposed workflow diagram.

Table 1. Graphical presentation of Fusion Matrix.

	Predicted class
True class	TP [True Positive]	FP [False Positive]
	FN [False Negative]	TN [True Negative]

Figure 2. ELM (RELM) architecture (Imron & Prasetyo, 2020).

Figure 3. ML test comparison chart for Heart Attack Analysis and Prediction dataset.

Figure 4. ML test comparison chart for the ‘Z-Alizadeh Sani dataset’.

Table 2. ML test comparison for the original Heart Attack Analysis and Prediction dataset.

ML and neural networks	ACCURACY	TIME
DT	79.5	22.17
LDA	83.8	1.66
LR	81.8	9.75
NB	81.5	2.85
SVM	83.8	1.79
KNN	82.2	1.03
ENSEMBLE	82.5	3.54
ELM (100 hidden neurons)	89.38	0.037
ELM (200 hidden neurons)	97.58	0.081

Table 3. ML test comparison for the original Z-Alizadeh Sani dataset.

ML and neural networks	ACCURACY	TIME
DT	77.9	24.69
LDA	81.7	2.86
LR	77.9	4.19
NB	72.2	8.35
SVM	82.2	1.93
KNN	81.2	2.73
ENSEMBLE	85.8	4.31
ELM (100 hidden neurons)	93.87	0.031
ELM (200 hidden neurons)	99.061	0.0547

Figure 5. Data that was intentionally lost ‘Z-Alizadeh Sani dataset’.

Table 4. ML test comparison for Heart Attack Dataset with and without missing data.

	ACCURACY original dataset	ACCURACY with missing data 3F*10R	ACCURACY with missing data 5F*10R	ACCURACY with missing data 10F*10R
DT	79.5	76.2	74.6	74.1
LDA	83.8	81.5	78.1	77.9
LR	81.8	80.8	76.9	73.01
NB	81.5	80.1	74.02	72.2
SVM	83.8	81.8	79.2	78.3
KNN	82.2	76.8	75.03	74.6
ENSEMBLE	82.5	79.5	79.2	79.1
ELM 200	97.58	97.35	93.5	89.6

Table 5. ML test comparison for ‘Z-Alizadeh Sani dataset’ with and without missing data.

	ACCURACY original dataset	ACCURACY with missing data 3F*10R	ACCURACY with missing data 5F*10R	ACCURACY with missing data 10F*10R
DT	78.9	77.9	77.2	76.3
LDA	81.7	79.2	78.3	77.1
LR	77.9	74.5	72.7	71.5
NB	72.6	70.1	70	69.7
SVM	82.8	80.2	80.01	79.4
KNN	81.2	75.2	74.4	74
ENSEMBLE	85.8	82.8	81.3	80.9
ELM 200	99.06	97.91	94.43	90.75

Table 6. Average estimated error table.

Dataset name	3F*10R missing		3F*10R missing		3F*10R missing
	AVERAGE	KNN	AVERAGE	KNN	AVERAGE	KNN
Heart Attack Analysis and Prediction	2.813%	2.432%	4.923%	4.155%	7.242%	6.952%
Z-ALIZADEH SANI	2.672%	2.511%	4.922%	4.344%	7.325%	6.822%

Figure 6. K-NN working process.

Table 7. ML test comparison for Heart Attack Dataset scores vs. other.

	(Yuan, 2021)	(Alhabib, 2022)	(Shi et al., 2021)	US	(Xu et al., 2021)	(Kwegyir et al., 2021)	(Dwivedi, 2018)	(Kavitha et al., 2021)	(Singh & Kumar, 2020)
DT				79.5				82.3	/
LR	/	/	87.0	81.1	/	/	83.3	/	/
SVM	/	/	83.1	83.1	/	/	/	83.2	/
KNN	83.8	/	/	82.2	/	/	84.8	/	88.5
ELM	/	/	/	97.6	/	/	/	/	/
RF	88.1	88.5	79.2	/	/	/	/	/	87.5
DL	/	/	/	/	/	/	94.2	/	/
ANN	/	/	82.22	/	90.01	88.7	/	/	/
FNN	/	/	/	/	/	89.2	/	/	/
hybrid	/	/	/	/	/	/	/	87	/

Figure 7. ML test comparison for Heart Attack Dataset with and without missing data.

Figure 8. ML test comparison ‘Z-Alizadeh Sani dataset’ with and without missing data.

Figure 9. ML test comparison of Heart Attack Dataset scores vs. others.

Figure 10. ML test comparison of Z-Alizadeh Sani dataset scores vs. others.

Table 8. ML test comparison for Z-Alizadeh Sani dataset scores vs. others.

	(Gupta et al., 2022)	(Gupta et al., 2021)	(Joloudari et al., 2022)	US	(Yuvalı et al., 2022)	(Ashish et al., 2021)	(Valarmathi & Sheela, 2021)	(Joloudari et al., 2022)
LR	/	88.6	/		92.4	/	/	/
SVM	/	86.8	/		/	/	/	83.3
SVM-XGBOOST	/	/	/	/	/	93.1	/	/
ELM	/	/	/	99.1	/	/	/	/
RF	97.4	94.7	/		/	/	95.4	/
DNN	/	/	98.79	/	/	/	/	92.18

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