Comparing machine learning models for osteoporosis prediction in Tibetan middle aged and elderly women.

ABSTRACT: The aim of this study was to establish the optimal prediction model by comparing the prediction effect of 6 kinds of prediction models containing biochemical indexes on the risk of osteoporosis in middle-aged and elderly women in Tibet. This study adopted a multi-stage cluster random sampling cross-sectional survey method. From January 2022 to January 2024, we obtained biochemical and bone mineral density (BMD) data from high altitudes in Tibet. We built a predictive model of osteoporosis in three steps. First, we performed feature selection to identify factors associated with osteoporosis. Next, the eligible participants were randomly divided into a training set and a test set in a ratio of 8:2. Then, the prediction model of osteoporosis was established based on Random Forest, ANN, XGB, a

SUBMITTER: Wang P 

PROVIDER: S-EPMC11958675 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature