[Retracted] Machine Learning-Based Automated Diagnostic Systems Developed for Heart Failure Prediction Using Different Types of Data Modalities: A Systematic Review and Future Directions

Table 4

Summary of image modality based research articles.

P_ID	Author	Technique	Data	Feature selection	Data sampling	Conclusion
PI_36	Nirschl et al. (2018) [83]	CNN+ whole-slide images of H&E tissue	209 patients	WND-CHARM	-fold crossvalidation	Accuracy: 97.4%
PI_37	Cetin et al. (2017) [84]	Radiomic approach + cardiac cine-MRI+ SVM	MICCAI 2017 challenge on automated cardiac diagnosis	Sequential forward feature selection (SFFS)	Crossvalidation	Accuracy: 98%
PI_38	Bai et al. (2016) [85]	SVM	STACOM 2015 dataset	ED + ES phases + PCA	-fold crossvalidation	Accuracy: 97.5%
PI_39	Qazi et al. (2007) [86]	SLFD	200 cases	LFD	ROC + -fold crossvalidation	Accuracy: 89.1%
PI_40	Sajn and Kukar (2011) [87]	Image processing + ML	288 patients	PCA	ROC + -fold crossvalidation	Accuracy: 81.3%
PI_41	R.Arsanjani et al.,(2015) [88]	Myocardial perfusion SPECT + ML	Cedars-Sinai Medical Center	LogitBoost	ROC + -fold crossvalidation	Accuracy: 81%
PI_42	Arsanjani et al. (2013) [89]	SPECT for detection of CVD	Cedars-Sinai Medical Center	LogitBoost	ROC + -fold crossvalidation	Accuracy: 87.2%
UPI_43	Udovychenko et al. (2015) [90]	-NN binary classification of heart failures	MCG data	Variance, kurtosis, and skewness	MMC	Accuracy: 80-88%
PI_44	Carneiro and Nascimento (2013) [91]	Multiple dynamic models and deep learning architectures	Hospital Fernando Fonseca dataset, 496 images	PCA	HMD, AV, MAD, AVP	d_HMD: 83%accuracy; d_AV: 91%accuracy; d_MAD: 94%accuracy; d_AVP: 83%accuracy.
PI_45	Zheng et al. (2008) [92]	3-D cardiac CT volumes using marginal space learning	Siemens Somatom Sensation	Steerable features	-fold crossvalidation	Mean error: 2.3%
PI_46	Berikol et al. (2016) [93]	SVM	Mersin University Research	N/A	-fold crossvalidation	Accuracy: 99.13%
PI_47	Lekadir et al. (2016) [94]	Plaque CNN architecture	Arnau de Vilanova	Deep learning CNN	-fold crossvalidation	Accuracy: 80%
PI_48	Sundaresan et al. (2017) [95]	Fully convolutional neural networks (FCN)	C.Ioannou	Rectified linear units (ReLUs)	ROC	Classification error rate: 23.48%
PI_49	Choi et al. (2016) [96]	Recurrent neural network	Sutter Palo Alto Medical Foundation	Gated recurrent unit GRU	-fold crossvalidation; =6	Accuracy: 88.3%
PI_50	Toth et al. (2018) [97]	Convolutional neural networks	LIDC-IDRI public dataset	(ReLU)	Qualitatively + quantitatively	Error rate: 2.92%
PI_51	Maraci et al. (2017) [98]	Analysis of linear ultrasound videos to detect fetal presentation and heartbeat	Dataset of 323 predefined free-hand videos	PCA	-fold crossvalidation; = 5	Accuracy: 93.1%
PI_52	Kurgan et al. (2001) [99]	Automated cardic SPECT diagnosis	Database of features(DF)	CLIP algorithm	Qualitative and Quantitative test	Accuracy: 83.08%
PI_53	Moreno et al. (2019) [100]	Multiscale motion for cardiac disease prediction	SPECT images dataset	RF + CLIP algorithm	F1-score + -fold crossvalidation	Accuracy: 51.06%; F1-score: 37.8%.
PI_54	Liu et al. (2016) [101]	ML prediction for cardiovascular	NSTEACS	PCA + MCE	-fold crossvalidation	Accuracy: 75%
PI_55	Shin et al. (2016) [102]	Deep convolutional neural networks for computer-aided detection	ImageNet dataset for CAD	CNN features of AlexNet pretrained + GoogleNet-RI	k-fold crossvalidation; = 5	Accuracy:95%
PI_56	Hisham et al. (2011) [103]	Grid independent technique	10 patients	Grid the images	Linear correlation	Accuracy:80%
PI_57	Allison et al. (2005) [104]	ANN	LAD model	Crossvalidation	Accuracy: 92%
PI_58	Welikala et al. (2017) [105]	Automated arteriole and venule classification using deep learning	UK Biobank	RGB and HSI color spaces	Crossvalidation	Accuracy: 86.97%
PI_59	Curiale et al. (2017) [106]	Deep learning network in cardiac MRI	Sunnybrook Cardiac Dataset (SCD)	RGB and HSI color spaces.	Dice’s coefficient	Accuracy: 90%
PI_60	Lindahl et al. (20197) [107]	Interpretation of myocardial SPECT perfusion images using ANN	Sunnybrook Cardiac Dataset (SCD)	Two-dimensional Fourier trans form technique	ROC + -fold crossvalidation; = 2	Sensitivity: 54.4%; Specificity: 70.5%
PI_61	Bai et al. (2015) [108]	Statistical parametric mapping(SPM) + linear model	Hammersmith Hospitals	PCA	Dice overlap metric + mean surface distance	LV_cavity: Myocardium: RVcavity:
PI_62	Moreno et al. (2019) [109]	Regional multiscale motion representation for cardiac disease prediction	Sunnybrook Cardiac Data (SCD)	Random Forest algorithm (RaF)	No. true positive over total of samples + F1-score	Accuracy: 77.83%; F1-score:76.92%
PI_63	Gulsun et al. (2016) [110]	Coronary centerline extraction + CNN	CTA datasets	CNN	Up-to-first-error evaluation	Sensitivity: 97%
PI_64	Narula et al. (2016) [111]	Automate morphological and functional assessments in 2D echocardiography	77 ATH+ 62 HCM patients	Information gain (IG) algorithm	-fold crossvalidation	Sensitivity: 96%
PI_65	Carneiro et al. (2011) [112]	Deep learning architectures and derivative-based search methods	Cohn-Kanade dataset (CK+)	PCA	ROC + HMD, HDF, MAD, MSSD	d_AVP: 95%
PI_66	Xu et al. (2012) [113]	Transient ischemic dilation for coronary artery disease in quantitative analysis	Nuclear Medicine Department, Sacred Heart Medical Center, Eugene	Mibi-Mibi TID	Standard deviation (SD)	Sensitivity: 76%
PI_67	Betancur et al. (2017) [114]	ML	Sacred Heart Medical Center	fold crossvalidation	Quantitative imaging analysis	Accuracy: 81%
PI_68	Coenen et al. (2018) [115]	ML + coronary computed tomographic	351 patients	ROC	ML-based CT-FFR model	Accuracy: 73%
PI_69	Wolterink et al. (2015) [116]	CNN	116 CT patients	-fold crossvalidation	ML-based CT-FFR model	Accuracy: 95%
PI_70	Nakazato et al. (2010) [117]	Perfusion imaging for detection of CAD	142 patients	N/A	-fold crossvalidation	Accuracy: 95%