Overview Understanding key machine learning algorithms is crucial for solving real-world data problems effectively.Data ...

Logistic regression, nearest neighbors regression, random forest regression (RFR), support vector regression, and K-nearest neighbors regression were the machine-learning algorithms used for this ...

In this context, using prognostic modelling approaches through machine learning technics emerges as a promising solution. Objectives We aim to assess the ability of several machine learning techniques ...

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

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 ...

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.

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 ...