A comprehensive analysis and performance evaluation for osteoporosis prediction models.

ABSTRACT: Medical data analysis is an expanding area of study that holds the promise of transforming the healthcare landscape. The use of available data by researchers gives guidelines to improve health practitioners' decision-making capacity, thus enhancing patients' lives. The study looks at using deep learning techniques to predict the onset of osteoporosis from the NHANES 2017-2020 dataset that was preprocessed and arranged into SpineOsteo and FemurOsteo datasets. Two feature selection methods, namely mutual information (MI) and recursive feature elimination (RFE), were applied to sequential deep neural network models, convolutional neural network models, and recurrent neural network models. It can be concluded from the models that the mutual information method achieved higher accuracy than recu

SUBMITTER: Shams Alden ZNAM 

PROVIDER: S-EPMC11784534 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature