Methods We trained models using logistic regression (LR) and four commonly used ML algorithms to predict NCGC from age-/sex-matched controls in two EHR systems: Stanford University and the University ...

EHR data may be particularly suitable for machine learning (ML) techniques, as such algorithms can process high-dimensional data and capture nonlinear relationships between variables. By comparison, ...

Various Machine Learning models such as Linear Regression, Logistic Regression, Random Forests and Deep Learning will be introduced to fit and classify biomedical data. Unsupervised learning ...

For the machine-learning analysis, they compared standard multivariable logistic regression, LASSO logistic regression, Random Forest and Extreme Gradient Boosting.

Random Forest and Extreme Gradient Boosting (XGBoost) were found to provide the highest performance for injury risk prediction. Logistic regression outperformed ML methods in 4 out of 12 studies.