# Deep-Learning Model to Predict Coronary Artery Calcium Scores in Humans from Electrocardiogram Data
## 4 Conclusions  
To obtain conventional CACS for detecting coronary heart disease risk, patients are required to have CT scans, which would involve exposure to radiation. This study introduces a deep-learning neural network model that uses ECG data to predict CACS. ECG is one of the more widely used diagnostic screening test procedures in cardiology, which is non-invasive and non-radiative. We introduce a pre-processing method to transform eight-lead ECG waves into one-cycle normalized waves. By using the normalized waves, our deep-learning neural network model can predict CACSs accurately. The neural network model is a  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Number of** **ECG Leads**} & \multirow{2}{*}{**CACS**} & \multicolumn{4}{c}{**AUC**} & \multicolumn{4}{c}{**Accuracy (\%)**} \\ \cline{3-8}  & & **Min** & **Max** & **Avg.** & **Min** & **Max** & **Avg.** \\ \hline \multirow{8}{*}{2} & 1 & 0.778 & 0.784 & 0.781 & 70.9 & 71.1 & 71.0 \\  & 25 & 0.796 & 0.801 & 0.798 & 72.3 & 72.5 & 72.4 \\  & 50 & 0.810 & 0.814 & 0.812 & 73.4 & 73.7 & 73.5 \\  & 100 & 0.823 & 0.826 & 0.824 & 74.0 & 74.7 & 74.4 \\  & 150 & 0.830 & 0.831 & 0.831 & 74.7 & 75.2 & 75.0 \\  & 200 & 0.837 & 0.838 & 0.837 & 75.3 & 75.7 & 75.5 \\  & 400 & 0.869 & 0.870 & 0.870 & 78.5 & 79.1 & 78.7 \\ \hline \multirow{8}{*}{6} & 1 & 0.767 & 0.782 & 0.775 & 70.3 & 71.1 & 70.6 \\  & 25 & 0.789 & 0.799 & 0.794 & 72.0 & 72.4 & 72.1 \\ \cline{1-1}  & 50 & 0.801 & 0.812 & 0.807 & 72.9 & 73.4 & 73.2 \\ \cline{1-1}  & 100 & 0.813 & 0.824 & 0.820 & 73.9 & 74.6 & 74.2 \\ \cline{1-1}  & 150 & 0.818 & 0.829 & 0.825 & 74.3 & 74.8 & 74.5 \\ \cline{1-1}  & 200 & 0.824 & 0.836 & 0.832 & 74.7 & 75.7 & 75.1 \\ \cline{1-1}  & 400 & 0.858 & 0.868 & 0.864 & 77.9 & 78.9 & 78.3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Test dataset performance according to the number of ECG leads.  
Figure 4: Classification performance for coronary artery disease risk: (a) receiver operating characteristic (ROC) curve when CACS is larger than 1; (b) ROC curve when CACS is larger than 400.  
CNN with 16 layers and 5 fully connected layers. The experimental results indicate that our network model could be employed to screen for coronary artery diseases. Our model does not use any CT scan, but uses a deep-learning model to predict CACSs with ECG datasets, which is safer, simpler, and less expensive than CT scans. Thus, our method is believed to have widespread implications and could significantly contribute to the existing knowledge on medical diagnosis. The morphology of ECG signals is often erratic, even for one person. This means that physical states such as running, walking, and sleeping may have significant effects on ECG signals. In this paper, only the participant's gender and age in the demographic information are used. To improve the performance of our model, we will consider additional demographic information such as ethnicity, marital status, smoking, and education. Additionally, our model will be extended into wearable devices (e.g., smart watches and smart patches) to provide information regarding coronary heart condition considering the user's lifestyle.  
[PERSON] and [PERSON] proposed the idea of this paper; [PERSON], [PERSON], and [PERSON] reviewed this paper and provided information; [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON], [PERSON], and [PERSON] reviewed the codes in this paper and wrote this paper. All authors have read and agreed to the published version of the manuscript.  
This research was supported by CDM-based precision medical data integration platform technology development program through the Korea Evaluation Institute of Industrial Technology funded by the Ministry of Trade, Industrial and Energy (No. 20005037), and by Chung-Ang University's research grant in 2020.  
The authors would like to thank Total Healthcare Center of Kangbuk Samsung Hospital for data collection.  
The authors declare no conflict of interest.