## 4 Conclusions  
Thus, the CNN model was developed for both single-lead and 12-lead ECG datasets compiled from clinical data. While 6630 datasets were used for the single-lead prediction model, 552 datasets were used for the 12-lead model. The clinical ECG signal data were gathered from a reputed hospital with additional features extracted through a unique feature extraction procedure. A hybrid method combining Pan-Tompkins and a statistical method involving feature extraction was implemented, which resulted in extracting all of the features related to the ECG. During the modeling, the CNN model with a single  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Single-Lead ECG Data**} & \multicolumn{2}{c}{**Twelve-Lead ECG Data**} \\ \cline{2-5}  & **Accuracy (\%)** & \
\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} & **Accuracy (\%)** &
\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} \\ \hline Split-1 & 99.8 & 0.006 & 98.7 & 0.021 \\ Split-2 & 99.5 & 0.011 & 99.4 & 0.013 \\ Split-3 & 99.3 & 0.01 & 99.3 & 0.011 \\ Split-4 & 99.3 & 0.012 & 98.8 & 0.019 \\ Split-5 & 99.7 & 0.008 & 99.1 & 0.017 \\ Average & 99.5\% & 0.009 & 99.1\% & 0.016 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Accuracies of the k-fold cross validation.  
Figure 18: Five-fold cross-validation technique.  
layer, two layers, and three layers experimented with different activation functions in different layers. Later, the three-layer CNN model was found to be the best architecture, with ReLU, ReLU, and LeakyReLU activation functions in the first, second, and third layers, respectively. This model showed an accuracy of 99% during the training phase, while it exhibited an accuracy of 98.6% in the validation trials. Thus, the three-layer CNN model, with ReLU, ReLU, and LeakyReLU activation functions works well for single-lead as well as 12-lead ECG datasets and is able to predict the CAD much more reliably than any other model. Hence, the present model proved to be the best fit for the classification of CAD. The same model can be implemented as an assisting tool for doctors in clinical applications.  
Conceptualization, S.T.V., S.S.R., N.K.P., A.K.S. and M.P.G.C.; methodology, S.T.V., S.S.R., N.K.P., C.M., P.K. and M.P.G.C.; software, A.K.S., M.P.G.C., S.T.V. and E.L.; validation, S.S.R., N.K.P., A.K.S., C.M. and P.K.; investigation, A.K.S., C.M., M.P.G.C., S.T.V. and E.L.; writing--original draft preparation, S.T.V., S.S.R., N.K.P., A.K.S., C.M., P.K. and M.P.G.C.; writing--review and editing. S.T.V., S.S.R., N.K.P., A.K.S., E.L. and M.P.G.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by a grant from the Romanian Ministry of Research, Innovation, and Digitalization, project number PFE 26/30.12.2021, PERFORM-CDI@UPT100--the increasing of the performance of the Polytechnic University of Timisoara by strengthening the research, development, and technological transfer capacity in the field of \"Energy, Environment and Climate Change\" at the beginning of the second century of its existence, within program 1--development of the national system of research and development, subprogram 1.2--institutional performance--institutional development projects--excellence funding projects in RDI, PNCDI III.  
Not applicable.  
The authors wish to acknowledge the help (via technical support and data collection) from the healthcare staff in the cardiography department of Kasturba Medical College, Mangalore.  
The authors declare no conflict of interest.