## 4 Discussion
Another promising application of artificial intelligence in the area of ischemic heart disease is presented by [PERSON] et al., who evaluated two different decision-making strategies for the type of diagnostic approach (anatomical study versus functional study) in cohorts of patients with suspected stable coronary artery disease. The two strategies compared were routine clinical practice versus a decision based on a machine learning algorithm. This algorithm was associated with a lower risk of adverse events [23]. Unlike the aforementioned studies [17; 18; 19; 20; 21; 22; 23], the current research focuses on assessing the prognostic value of a specific clinical variable, namely the presence of non-obstructive coronary artery disease. Consequently, our aim is not to compare the predictive capacity of two different methodological approaches (classical statistics versus AI) but to evaluate the problem from two distinct perspectives to obtain complementary results. The machine learning models we developed for the aforementioned objective exhibited adequate accuracy (ranging from 0.81 for decision trees and naive Bayes classifier to 0.87 for logistic regression and support vector machines) when compared to the performance of other AI models applied to predicting cardiovascular events in the context of coronary and cardiovascular diseases [18; 24; 25]. However, the results were insufficient for recall (ranging from 0 to 0.33), precision (ranging from 0 to 0.41), and F1-score (ranging from 0 to 0.29) metrics. The confusion matrix confirms that the developed models performed well in classifying patients without events but showed inadequate capability in identifying patients with events. While acknowledging the limitations of the predictive models developed in this study, the SHAP analysis indicated that non-obstructive coronary disease may be a potential risk factor for the development of cardiovascular disease during follow-up. The consistency observed in the association between non-obstructive coronary artery disease and long-term cardiovascular events, confirmed through various data analysis tools, strengthens the causal relationship between the former and the latter as consistency is widely recognized as an essential criterion for establishing causal relationships [26]. The primary future implication that emerges from these results is that there is stronger scientific evidence supporting the need to enhance research on the efficacy of various therapeutic strategies (diet, exercise, and pharmacological treatment) in patients with a common condition that has previously received very little attention, namely, the presence of non-obstructive coronary artery disease [27]. Regarding the study sample, it is worth noting several peculiarities. Firstly, it included a substantial percentage of both stable and unstable patients, which is rare in studies evaluating the prognostic value of obstructive coronary lesions. Previous works have typically focused on chronic coronary syndrome patients [2; 28; 29; 30; 31], with limited evidence for the prognostic value of non-obstructive coronary artery disease in the context of acute coronary syndrome [32; 33]. The second peculiarity of the sample lies in its classification into three specified groups (lesions less than 20%, lesions between 20% and 50%, and lesions greater than 70%), excluding patients with borderline lesions (lesions between 50% and 70%), which have been ambiguously considered as obstructive [2] or non-obstructive in the previous literature [28; 34]. This study has several limitations that must be acknowledged, in addition to those already discussed regarding the performance of the models. Firstly, this is a retrospective study, which carries an inherent risk of biases due to the lack of collection of certain variables and the absence of randomization, primarily leading to confounding factors in the latter case.