Ali Ezzati1,2, Andrea R Zammit1, Richard B Lipton1,2. 1. Department of Neurology, Albert Einstein College of Medicine. 2. Department of Neurology, Montefiore Medical Center, Bronx, NY.
Abstract
BACKGROUND: Automatic classification techniques provide tools to analyze complex data and predict disease progression. METHODS: A total of 305 cognitively normal; 475 patients with amnestic mild cognitive impairment (aMCI); and 162 patients with dementia were included in this study. We compared the performance of 3 different methods in predicting progression from aMCI to dementia: (1) index-based model; (2) logistic regression (LR); and (3) ensemble linear discriminant (ELD) machine learning models. LR and ELD models were trained using data from cognitively normal and dementia subgroups, and subsequently were applied to aMCI subgroup to predict their disease progression. RESULTS: Performance of ELD models were better than LR models in prediction of conversion from aMCI to Alzheimer dementia at all time frames. ELD models performed better when a larger number of features were used for prediction. CONCLUSION: Machine learning models have substantial potential to improve the predictive ability for cognitive outcomes.
BACKGROUND: Automatic classification techniques provide tools to analyze complex data and predict disease progression. METHODS: A total of 305 cognitively normal; 475 patients with amnestic mild cognitive impairment (aMCI); and 162 patients with dementia were included in this study. We compared the performance of 3 different methods in predicting progression from aMCI to dementia: (1) index-based model; (2) logistic regression (LR); and (3) ensemble linear discriminant (ELD) machine learning models. LR and ELD models were trained using data from cognitively normal and dementia subgroups, and subsequently were applied to aMCI subgroup to predict their disease progression. RESULTS: Performance of ELD models were better than LR models in prediction of conversion from aMCI to Alzheimer dementia at all time frames. ELD models performed better when a larger number of features were used for prediction. CONCLUSION: Machine learning models have substantial potential to improve the predictive ability for cognitive outcomes.
Authors: Andrea R Zammit; Graciela Muniz-Terrera; Mindy J Katz; Charles B Hall; Ali Ezzati; David A Bennett; Richard B Lipton Journal: J Alzheimers Dis Date: 2019 Impact factor: 4.472
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Authors: Ali Ezzati; Andrea R Zammit; Danielle J Harvey; Christian Habeck; Charles B Hall; Richard B Lipton Journal: J Alzheimers Dis Date: 2019 Impact factor: 4.472
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