A new UCLA study reveals Alzheimer’s may not strike randomly, and early warning signs may be hiding in your medical history.

Researchers have identified sequential diagnostic patterns that may enhance early detection and improve strategies for disease prevention. Researchers at UCLA Health have uncovered four distinct ...

The existing dementia risk models are limited to known risk factors and traditional statistical methods. We aimed to employ machine learning (ML) to develop a novel dementia prediction model by ...

A single, freely available, noninvasive brain scan done in just a few minutes during midlife can predict what chronic ...

Cardiovascular disease (CVD) is the leading cause of death worldwide. A Machine Learning (ML) system can predict CVD in the early stages to mitigate mortality rates based on clinical data. Recently, ...

Identifying the progression stages of Alzheimer’s disease (AD) can be considered as an imbalanced multi-class classification problem in machine learning. It is challenging due to the class imbalance ...