Multiple machine learning algorithms were trained, including XGBoost, Random Forest, LightGBM, Ridge Regression, Lasso Regression, Support Vector Regression (SVR), Decision Tree Regressor (DTR), ...

In this paper, by applying machine learning techniques, we propose to predict stock prices based on trends from previous years’ stock data using learning models, such as Linear Regression, MLP ...

An automated insulin delivery system controlled by an artificial intelligence machine learning model may perform just as well as a system using a traditional equation-based insulin dosing ...

The performance of different machine learning models in detecting hepatitis among people with diabetes.

The same type of machine learning methods used to pilot self-driving cars and beat top chess players could help type-1 diabetes sufferers keep their blood glucose levels in a safe range.

After dimensionality reduction of the data, the following different machine learning techniques were applied with variations of parameter settings to build the models, such as: linear regression ...

The risk for poor glycemic control in patients with type 2 diabetes can be predicted with confidence by using machine learning methods, a new study from Finland finds.

Researchers created a machine-learning–based model to help predict which patients will develop diabetes, according to an abstract to be published in the Journal of the Endocrine Society.