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This article proposes that machine learning can break through the technical limitations of the linear growth test for the early physique of infants smaller than gestational age and can accurately calculate and predict the consequences of the disease. For testing the linear growth of the early physique of infants smaller than gestational age, the data collection and judgment are carried out according to the computer analysis method. Experimental results show that 47.3% of infants younger than gestational age may have suffocation. The experimental subjects designed in this study are small-for-gestational-age infants who were hospitalized in the neonatal intensive care unit from January 2020 to January 2021. According to the relationship between gestational age and birth weight, the survey subjects were divided into two groups: early group and late group. Male and female small-for-gestational-age infants accounted for 68% and 32%, respectively. Among them, the proportion of early gestational age was the most, with more boys than girls, and sick singleton was more than twins. In the early group, the incidence was 52.1% for neonatal asphyxia, 22.5% for feeding intolerance, 14.8% for intracranial hemorrhage, 6.3% for scleredema, 24.7% for neonatal hyperbilirubinemia, 24.6% for hypoglycemia, 1.1% for apnea, and 3.2% for respiratory distress syndrome. Infants develop differently at different stages of corrected gestational age. The incidence of low body weight (6%) after correction for 3 months was significantly reduced compared with correction for gestational age, and the difference was statistically significant ( P  < 0.05). The nutrient absorption of infants younger than gestational age can promote physical catch-up growth, physical development, and neurodevelopment. Therefore, the physical growth of infants younger than gestational age requires supplementation that focuses on nutrition.

Early Physical Linear Growth of Small-for-Gestational-Age Infants Based on Computer Analysis Method