Optimizing Machine Learning Methods to Improve Predictive Models of Alzheimer's Disease.

Journal: Journal of Alzheimer's disease : JAD

PMID: 31476152

Abstract

BACKGROUND: Predicting clinical course of cognitive decline can boost clinical trials' power and improve our clinical decision-making. Machine learning (ML) algorithms are specifically designed for the purpose of prediction; however. identifying optimal features or algorithms is still a challenge.

Authors

Ali Ezzati

Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA.
Andrea R Zammit

Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA.
Danielle J Harvey

Department of Public Health Sciences, University of California-Davis, Davis, CA, USA.
Christian Habeck

Department of Neurology, Cognitive Neuroscience Division, Columbia University, New York, NY, USA.
Charles B Hall

Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA.
Richard B Lipton

Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA.

Keywords

Aged Aged, 80 and over Alzheimer Disease Apolipoproteins E Cerebral Cortex Cognitive Dysfunction Demography Discriminant Analysis Female Hippocampus Humans Longitudinal Studies Machine Learning Magnetic Resonance Imaging Male Middle Aged Models, Neurological Predictive Value of Tests Prognosis Support Vector Machine

External Resources

View on PubMed Access via DOI PubMed (31476152)

Optimizing Machine Learning Methods to Improve Predictive Models of Alzheimer's Disease.

Abstract

Authors

Keywords

External Resources

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