Table 2.

Preprocessing and predictive methods.

Study	Dataset	Preprocessing and Modeling	Results
Algarni et al. [27]	Coronary artery X-ray angiography images obtained from a clinical database.	Training: 100 images Test: 30 images ASCARIS model (based on color, diameter, and shape features).	Accuracy: 97%
Uyar and İlhan [30]	Cleveland dataset for heart disease.	Removal of 6 instances with missing entries from the dataset and categorization of the diagnosis attribute (num) into two classes: absence (num = 0) and presence (num = 1, 2, 3, or 4) of heart disease. Recursive Fuzzy Neural Network (RFNN)	Testing set accuracy: 97.78% Overall accuracy: 96.63%
Deng et al. [31]	Fuwai ECG database and public PTB database	training phase for dynamics acquisition and a test phase for dynamics reuse Attention-based Res-BiLSTM-Net model	F1 scores ranging from 0.72 to 0.98
Das et al. [32]	UCI dataset	SAS-based software Neural Networks	Training accuracy: 86.4%, Validation accuracy: 89.011%