首页|New Machine Learning Study Findings Recently Were Reported by a Researcher at Ho pe College (Predicting newborn birth outcomes with prenatal maternal health feat ures and correlates in the United States: a machine learning approach using arch ival ...)
New Machine Learning Study Findings Recently Were Reported by a Researcher at Ho pe College (Predicting newborn birth outcomes with prenatal maternal health feat ures and correlates in the United States: a machine learning approach using arch ival ...)
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2024 OCT 03 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on ar tificial intelligence. According to news originating from Hope College by NewsRx correspondents, research stated, "Newborns are shaped by prenatal maternal expe riences. These include a pregnant person's physical health, prior pregnancy expe riences, emotion regulation, and socially determined health markers." The news journalists obtained a quote from the research from Hope College: "We u sed a series of machine learning models to predict markers of fetal growth and d evelopment-specifically, newborn birthweight and head circumference (HC). We use d a pre-registered archival data analytic approach. These data consisted of mate rnal and newborn characteristics of 594 maternal-infant dyads in the western U.S . Participants also completed a measure of emotion dysregulation. In total, ther e were 22 predictors of newborn HC and birthweight. We used regularized regressi on for predictor selection and linear prediction, followed by nonlinear models i f linear models were overfit. HC was predicted best with a linear model (ridge r egression). Newborn sex (male), number of living children, and maternal BMI pred icted a larger HC, whereas maternal preeclampsia, number of prior preterm births , and race/ethnicity (Latina) predicted a smaller HC. Birthweight was predicted best with a nonlinear model (support vector machine). Occupational prestige (a m arker similar to socioeconomic status) predicted higher birthweight, maternal ra ce/ethnicity (non-White and non-Latina) predicted lower birthweight, and the num ber of living children, prior preterm births, and difficulty with emotional clar ity had nonlinear effects."
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