Constructing a fall risk prediction model for hospitalized patients using machine learning.

ABSTRACT:

Study objectives

This study aimed to identify the risk factors associated with falls in hospitalized patients, develop a predictive risk model using machine learning algorithms, and evaluate the validity of the model's predictions.

Study design

A cross-sectional design was employed using data from the DRYAD public database.

Research methods

The study utilized data from the Fukushima Medical University Hospital Cohort Study, obtained from the DRYAD public database. 20% of the dataset was allocated as an independent test set, while the remaining 80% was utilized for training and validation. To address data imbalance in binary variables, the Synthetic Minority Oversampling Technique combined with Edited Nearest Neighbors (SMOTE-ENN) was applied. Univariate analysis and l

SUBMITTER: Kang CW 

PROVIDER: S-EPMC11748310 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature