When the blood flow to the brain is cut off, a stroke develops. A stroke can often result in long-term damage. And the road to recovery from a stroke is long and winding. With this in mind, an international team of scientists led by EPFL has created a system that analyzes and predicts the future of stroke victims using data from the brain’s connectome and machine learning. The data was fed into a “support-vector machine,” or SVM, which is a type of machine-learning system that utilizes examples to translate an input into an output. The SVM was trained to identify between individuals who recovered naturally and those who did not have their whole-brain structural connectomes recovered naturally.
Source: https://www.techexplorist.com/improving-stroke-recovery-prediction-using-machine-learning/40015/
