Random forest model for feature-based Alzheimer's disease conversion prediction from early mild cognitive impairment subjects
Matthew Velazquez, Yugyung Lee, Alzheimer’s Disease Neuroimaging Initiative
This study uses a balanced random forest model to predict Alzheimer's disease conversion from early mild cognitive impairment patients using clinical features, achieving 93.6% accuracy.