An earlier serial lactate determination analysis of cardiac arrest patients using a medical machine learning model

In general, cardiac arrest results in an inability to pump enough blood for the body's needs. Through this, the organs and tissues get enough oxygen and nutrients for their metabolic needs, and excess fluid accumulates. This further increases the severity of the patient's problems. Cardiac arrest is the leading cause of hospitalization in people over 65 and is therefore considered a significant public health problem. It is a pathology linked to an increase in average life expectancy, and its incidence is increasing every year due to the general ageing of the population. In this paper, an earlier serial lactate determination analysis of cardiac arrest patients using a medical machine learning algorithm. It can occur following an episode of cardiac arrest or inadequate treatment of chronic diseases, including diabetes and hypertension. Adopting lifestyle habits that prevent the onset of these conditions is an essential strategy for preventing heart failure. The proposed approach has achieved 91.58% accuracy, 87.96% precision, 84.62% recall, and 85.23% F1-score results compared to other methods.