Development of Machine Learning Models for Prediction of Osteoporosis from Clinical Health Examination Data.

ABSTRACT: Osteoporosis is treatable but often overlooked in clinical practice. We aimed to construct prediction models with machine learning algorithms to serve as screening tools for osteoporosis in adults over fifty years old. Additionally, we also compared the performance of newly developed models with traditional prediction models. Data were acquired from community-dwelling participants enrolled in health checkup programs at a medical center in Taiwan. A total of 3053 men and 2929 women were included. Models were constructed for men and women separately with artificial neural network (ANN), support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), and logistic regression (LoR) to predict the presence of osteoporosis. Area under receiver operating characteristic curve (AUROC) wa

SUBMITTER: Ou Yang WY 

PROVIDER: S-EPMC8305021 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature