## 4 Discussion  
This study is the first to apply ML methods to the DukeCath database, which contains 155,980 catheterization procedures to predict short-term risks for CVDs. In this research, various ML methods (linear models, nonlinear models, and a neural network) were applied for short-term likelihood assessment of varied cardiovascular events and treatments (including CABG, PCI, VS, stroke, and MI), PCI treatments, and CABG treatments for patients, who were not previously diagnosed with CVD, by inputting their first catheterizations results, catheterizations procedure, medical history, vital signal, laboratory results, and physical examination prior to catheterizations. The RFC was the best predictive model among all the ML models in the risk assessment on varied cardiovascular events and treatments section with high accuracy. This model accurately identifies the high-risk patients who will experience multiple cardiovascular incidents within 90 days. In the case of the PCI prediction, the GBC was the best model among all the ML models with high accuracy. As for the CABG forecast, the GBC was the best model among all the ML models, with high prediction accuracy. The PCI and CABG treatments were predicted accurately in a short-term manner, providing instance diagnosis assistance in cardiovascular treatment planning. Some of the patients are expected to have recurrent cardiac events within the first year after catheterization. This highlights the poor medical outcomes and high utilization of resources by this patient population [28]. Our study provides a better solution for determining recurrent cardiovascular events and identifying high-risk patients. This work also presents a baseline for ML applications targeting the treatment predictions for suspected CVD patients and serves as a comparison reference for future ML model validation. In some survival analyses on CVD patients, African-Americans are shown to have lower long-term survival than Caucasian [29]. Extremes of BMI are associated with lower long-term survival in patients with significant coronary disease [30]. In our study, race is observed to be significant only in the statistical analysis of the short-term effect, but it is a weak feature in all three prediction cases. Contrarily, the BMI feature is not significant in our statistical analysis, but it is a strong feature in the risk estimations (Case I predictions). Our ML applications examined the interactions/relations between features, and the weak features were efficiently considered. Despite having a high significance in statistical analysis, a feature will process with low importance if it lacks interactions between other features. Therefore, the feature importance presented in our study provides additional importance evaluations for CVD patients' features. Among the other ML applications on CVD, cardiovascular symptoms were broadly used as the targets and predicted as output. The naive Bayes algorithm was used to predict heart attacks with an accuracy of 81.25% [31]. This naive Bayes classifier reduced the doctor's efforts and time by automating the risk prediction. In another study, a deep neural network achieved an F1 score of 0.092 and AUROC of 0.835 on MI predictions from harmonized EHR data [10]. With the ensemble deep learning method, 85% of heart arrest was successfully predicted one hour before the incidence (sensitivity \(\geq\) 0.85), and 73% of arrest cases 25 h before the occurrence (sensitivity \(\geq\) 0.73) [32]. The models from these studies could predict adverse cardiovascular conditions based on the known factors which have been widely accepted to directly relate to the predicted cardiovascular events. Therefore, these methods might not be suitable for patients without previously diagnosed CVD due to the lack of previous adverse cardiovascular symptoms. Additionally, since the risk of each CVD patient is not the same, different CVD patients may develop different cardiovascular events, and the exact cardiovascular event that will occur is unknown.