Machine learning IDs biomarkers for Alzheimer’s disease

Researchers at the University of Southern California Viterbi School of Engineering have utilized machine learning to detect clusters of potential biomarkers of Alzheimer’s disease. This will allow for earlier diagnosis and could potentially lead to non-invasive methods of tracking the progression of the disease in impacted patients. Findings were published in the journal Frontiers in Aging NeuroScience.