## 5 Discussion  
Because of the tremendous advances in PCI instruments and technology, more patients with multiwessel CAD prefer to undergo PCI instead of CABG. Before deciding whether or not to accept surgery, the doctor's clinical experience and SYNTAX score are used to make a preliminary judgment for the patient, and the SYNTAX is based on the severity of the blood vessels. SYNTAX score, which is a score according to coronary artery morphology, was used for the strategy of PCI or CABG for patients with triple vessel disease. For patients with low SYNTAX scores, PCI is an acceptable revascularization strategy, although at a price of significantly higher rates of repetitive revascularization [47]. The SYNTAX score and both are useful determinants for predicting hard clinical events (HCE: death, nonfatal myocardial infarction, and stroke). The incidence of HCE at three years significantly differed according to Clinical SYNTAX score (High, 20.2%; Intermediate, 1.2%; and Low, 6.0%; log-rank \(p<0.001\)), but not according to SYNTAX score (High, 14.0%; Intermediate, 5.8%; and Low, 7.3%; log-rank \(p=0.13\)) [48]. It is dangerous if we cannot judge only by looking at a single indicator for major decisions concerning life and death. Disease history and surgical factors are also useful for predicting patient prognoses after an intervention. Based on the risk features, we constructed an ensemble machine learning model using five well-known machine learning models and used majority voting to develop the best prediction model. Renal insufficiency is a risk factor for poor long-term survival in patients who underwent coronary angiography [49], whereas age affects early and late survival independently after PCI [50]. Moreover, a history of CHF, acute myocardial infarction, cardiogenic shock, and renal disease is significantly associated with mortality [20; 51; 52]. MORidity and mortality for PCI are high for patients with CAD and kidney disease [49; 51; 53; 54; 55]. The ISCHEMIA CKD trial included 777 patients with advanced renal insufficiency (eGFR \(<30\) mL/min) in the context of the larger ISCHEMIA trial population. An early routine invasive strategy failed to reduce the incidence of death or myocardial infarction, and an excess of stroke, death, or the initiation of dialysis was observed more than medical therapy alone [56]. CHF is a strong predictor of adverse outcomes after PCI [57]. Bleeding events were also associated with early and late mortality [58]. Some models were developed using elderly patient databases or fewer data to capture the important risk factors and prepare angiographic and mortality prediction models [52; 18; 59]. Moreover, feature selection can help physicians understand the causes of a disease; this method has been successfully applied in the medical industry [60]. The top 15 risk factors were identified using five single different machine learning models, and the rank of the scores for each variable was averaged. Figure 2 presents the ranking of important variables for predicting the outcome of PCI; age, CHF, and CRF are the top predictors of mortality, which is consistent with previous studies. Moreover, disease history and surgical risk factors were also identified as risk factors in this study. Although the safety of PCI has improved substantially, severely ill patients with different underlying diseases or complications who underwent 3-vessel PCI may experience prolonged hospital stays [58]. Figure 3 demonstrates the rules of the decision tree: patients aged 68-75 years with CRF will possibly have a 94% death rate. History of CRF should thus be confirmed before undergoing 3-vessel PCI for the first time; CABG or staged PTCA may be the preferred strategy for such patients. This study demonstrated that feature selection, applying ensemble learning, and the presence of fewer risk factors could help achieve higher accuracy and identify factors affecting survival.