A machine learning-based heart disease prediction model (ML-HDPM) that uses various combinations of information and numerous recognized categorization methods.

For example, by preventing hospitalizations in cases of just two widespread chronic illnesses — heart disease and diabetes — the United States could save billions of dollars a year.

After feature selection, modeling was conducted using a 10-fold cross validated boosted ensembles approach, a machine learning algorithm called LogitBoost, to analyze clinical, PET, and CCTA data.

The use of machine learning, says Keane, can overcome this challenge. Using AI to diagnose disease from eye scans has proven to be one of the fastest-developing fields of machine learning medicine.

This method is applied to a use case of water temperature prediction in the Delaware River Basin (DRB) and is designed to overcome some of the common pitfalls of prediction using machine learning ...