A Preliminary Study on Data Mining Algorithm on Glucose Metabolism in Pregnancy
Data mining is an interdisciplinary subfield of computer science. It is the computational process of discovering patterns in large data sets (“big data”) involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use.
Pregnancy is a state of ever-increasing fetal demand for fuel. This demand is met through increased caloric intake, hyperinsulinemia, insulin resistance, and maternal pancreatic islet hypertrophy. In addition, fasting in the pregnant state results in maternal hypoglycemia, elevated plasma lipid concentrations, and hypo aminoacidemia. These maternal adaptive changes serve the unique purpose of self-preservation, with an attempt to use lipid as an alternative fuel in the face of the uninterrupted siphoning of glucose and amino acids to the fetus.