Background: Hip fracture (HF) is one of the most prevalent orthopedic conditions among the elderly, with falls being the primary risk factor for HF. With the surge of aged population, China is facing great challenges from HF and falls. However, a comprehensive long-term observation of risk factors affecting HF and falls and their association are little reported at a national level.
View Article and Find Full Text PDFObjective: To use routine demographic and clinical data to develop an interpretable individual-level machine learning (ML) model to diagnose knee osteoarthritis (KOA) and to identify highly ranked features.
Methods: In this retrospective, population-based cohort study, anonymized questionnaire data was retrieved from the Wu Chuan KOA Study, Inner Mongolia, China. After feature selections, participants were divided in a 7:3 ratio into training and test sets.