Paul N Thompson, Emily J Tomayko, Katherine B Gunter, John Schuna, Megan McClelland
Impacts of the Four-Day School Week on Early Elementary Achievement
Early childhood research quarterly
early diagnosis hypertension ht crucial preventing endorgan damage study aims identify risk factors future ht young individuals application machine learning ml models study included individuals aged 1840 years diagnosed ht ambulatory blood pressure monitoring abpm participants monitored hypertension diagnosis date abpm application date data collection hypertension prediction carried using three distinct ml methods support vector machine random forest least absolute shrinkage selection operator identification variables significant future ht based outcomes models study comprised 516 patients mean followup duration 7934586 days following integration demographic data laboratory results abpm findings ml models age highdensity lipoprotein cholesterol triglycerides standard deviation systolic blood pressure sdsis identified predictors future ht logistic regression selected variables age diabetes mellitus history hdl triglycerides white blood cell count sdsis using full data set gave following log odds 00737 p0001 07146 p0001 00160 p0071 00026 p0002 00857 p0069 00850 p0005 respectively corresponding probability values age diabetes mellitus history hdl triglycerides white blood cell count sdsis 05184 06714 04960 05006 05214 05212 respectively indicates unit increase factors except diabetes mellitus history increases probability future ht 50 history diabetes however increases probability future ht two thirds history diabetes mellitus emerged crucial predictor future ht across applied methods ml methods appear valuable tools predicting future ht widespread adoption methods refinement comprehensive models lay groundwork future studies