Introduction
critically ill, Machine learning, heart or lung failure
CRITICALLY ILL
Patients with severe heart or lung failure can benefit from extracorporeal membrane oxygenation (ECMO). ECMO was required for critically ill patients with guarded prognoses regardless of the initial etiology [To predict outcomes in patients receiving ECMO support, risk scores have been developed, such as the Survival After Veno-Arterial ECMO (SAVE) Score and the ECMO-ACCEPTS Score [The highly correlated variables included in the analysis could also be a significant reason for this disequilibrium. Despite the fact that these clinical parameters represent damage to different organs, there is interaction between these organs, and they are always interconnected. Due to this, it is vital to clarify this issue using a different method than the traditional one. Using newly developed machine learning algorithms to perform supervised classification, it sheds light on a distinct method of clarifying this issue [Machine learning algorithms employ various methods, such as probabilistic and optimization approaches, to learn from past experience and detect useful patterns in large, unstructured and complex data sets [