Caitlin Kothe, Breanna Reynolds, Kareaion Eaton, Sarah Harrison, Alex Kozsalinski, Monica Krogmann, Hannah Norton, Ann Pharr, Ahmad Sabbahi, Kerry Volansky
The Impact of Virtual Laboratories on Student Clinical Education Preparedness: A Mixed-Method Analysis
Journal, physical therapy education
diabetes chronic condition result many longterm physiological metabolic neurological complications therefore early detection diabetes would help determine proper diagnosis treatment plan study employed machine learning ml based casecontrol study diabetic cohort size 1000 participants form qatar biobank predict diabetes using clinical bone health indicators dual energy xray absorptiometry dxa machines ml models utilized distinguish diabetes groups nondiabetes controls recursive feature elimination rfe leveraged identify subset features improve performance model shap based analysis used importance features support explainability proposed model ensemble based models xgboost rf achieved 84 accuracy detecting diabetes applying rfe selected 20 features improved model accuracy 872 clinical standpoint higher hdlcholesterol neutrophil levels observed diabetic group along lower vitamin b12 testosterone levels lower sodium levels found diabetics potentially stemming clinical factors including specific medications hormonal imbalances unmanaged diabetes believe dapagliflozin prescriptions qatar associated decreased gamma glutamyltransferase aspartate aminotransferase enzyme levels confirming prior research observed bone area bone mineral content bone mineral density slightly lower diabetes group across almost body parts difference control group statistically significant except t12 troch trunk area significant negative impact diabetes progression bone health observed period 515 yrs cohort study recommends inclusion ml model combines dxa clinical data early diagnosis diabetes