## 4 Discussion  
This study aims to apply a novel approach to evaluate the prognostic value of non-obstructive coronary artery disease by utilizing various machine learning techniques. The SHAP analysis applied to the model developed using the support vector machine indicates that the presence of non-obstructive coronary artery disease (lesions between 20% and 50%), as opposed to normal coronary arteries (lesions \(\leq\) 20%), emerged as a predictive variable for cardiovascular events in patients undergoing coronary angiography. However, all the machine learning models presented in this work exhibit a very low true positive rate, limiting their validity in predicting cardiovascular events. Leveraging classical statistical techniques, we previously observed a roughly 70% increased risk of cardiovascular events in patients with non-obstructive coronary artery disease compared to patients with normal coronary arteries. [PERSON] et al. previously found that both obstructive and non-obstructive lesions of the left main and proximal left anterior descending coronary arteries were associated with a higher risk of clinical events and benefited from a prolonged dual antiplatelet therapy course [13]. Over time, different studies have illustrated different applications of AI in clinical settings. One approach that has garnered significant interest is the automatic interpretation of images for diagnosis or prognosis in various pathologies. In this context, several studies have demonstrated that the diagnosis of coronary artery disease can be improved through machine learning algorithms designed for the interpretation of non-invasive cardiac imaging studies (single photon emission computed tomography-myocardial perfusion imaging) [14] or images obtained via invasive coronary angiography [15; 16]. Another promising aspect is the development of predictive models for major clinical events under different baseline conditions, basing this prediction on clinical parameters without performing any type of automatic or semi-automatic image analysis. Moreover, in various pathological contexts, AI has shown improved predictive capabilities compared to classical statistical techniques. For instance, [PERSON] et al. compared the utility of AI  
Figure 2: SHAP model of contribution values. versus classical statistics in predicting the survival of patients admitted to Intensive Care Units [17]. Similarly, [PERSON] et al. analyzed the predictive capacity of AI models for mortality in hospitalized heart failure patients, outperforming classical statistical methods [18]. Additionally, [PERSON] et al. presented an innovative approach using a two-stage machine learning model for predicting the co-occurrence of diabetes mellitus and cardiovascular disease, showcasing the added value of AI over traditional statistical techniques, which have mainly focused on predicting isolated events based on multiple variables [19]. Similarly, [PERSON] et al. developed a model using Extreme Gradient Boosting that achieved a high predictive capacity for cardiovascular disease in a large sample of patients with chronic kidney disease [20]. Other studies have also explored the value of machine learning models to predict the development of coronary artery disease (excluding other forms of cardiovascular disease), demonstrating the prognostic value of simple parameters such as the TyG index [21]. A subsequent step currently being undertaken is the development of machine learning models to predict short- or long-term prognosis in established coronary artery disease. A relevant example is the work of [PERSON] et al., who developed an algorithm using Hierarchical Agglomerative Clustering, which improved the prediction of one-month mortality in patients who had suffered ST-elevation acute coronary syndrome [22].