THE USE OF MACHINE LEARNING ALGORITHMS FOR THE PREDICTION OF BLOOD LACTATE LEVELS IN CHILDREN AFTER CARDIAC SURGERY

Abstract

In order to pinpoint the origin of the disease, the conventional LHC measures the fundamental biochemical parameters; a more comprehensive LHC incorporates The LDH parameter may provide more specific information on the origin, kind, and cause of pathology. During glycolysis, the enzyme lactate dehydrogenase catalyzes the process that forms lactic acid. As with the majority of catalysts, lactate dehydrogenase is rapidly and uniformly eliminated from the body upon formation. One of the main diagnostic tools available is the laboratory blood test. Their findings are used to assess potential disruptions in the operation of various bodily systems and organs. Determine hematological, cardiac, muscular, and ontological diseases using LDH in biochemical blood tests. The liver and kidney parenchyma have a high quantity of enzymes. Additionally, it may be found in the heart and skeletal muscles. An isoenzyme is specific to each area of localization. There is a trace quantity of lactate dehydrogenase in RBCs. Using Machine Learning Algorithms, this research discusses a clever way to predict blood lactate levels in children following cardiac surgery. The majority of the time, a rise in enzyme concentration is the unpleasant outcome of a biochemical blood test for LDH. This is due to the fact that a considerable amount of lactate dehydrogenate enters the circulation when an organ's cellular structure is harmfully compromised. When liver cancer and cirrhosis progress to their degenerative stages, enzyme levels drop or disappear entirely.