PrOsteoporosis: predicting osteoporosis risk using NHANES data and machine learning approach.

ABSTRACT:

Objectives

Osteoporosis, prevalent among the elderly population, is primarily diagnosed through bone mineral density (BMD) testing, which has limitations in early detection. This study aims to develop and validate a machine learning approach for osteoporosis identification by integrating demographic data, laboratory and questionnaire data, offering a more practical and effective screening alternative.

Methods

In this study, data from the National Health and Nutrition Examination Survey were analyzed to explore factors linked to osteoporosis. After cleaning, 8766 participants with 223 variables were studied. Minimum Redundancy Maximum Relevance and SelectKBest were employed to select the import features. Four Machine learning algorithms (RF, NN, LightGBM and XGBoost.) were ap

SUBMITTER: Si Z 

PROVIDER: S-EPMC11899459 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature