Comparing performance of different predictive models in estimating disease progression in Alzheimer disease

Ali Ezzati, Andrea R. Zammit, Richard B. Lipton

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
JournalAlzheimer Disease and Associated Disorders
DOIs
StateAccepted/In press - 2021

Keywords

  • Alzheimer disease
  • Dementia
  • Machine learning
  • MCI
  • Predictive analytics

ASJC Scopus subject areas

  • Clinical Psychology
  • Gerontology
  • Geriatrics and Gerontology
  • Psychiatry and Mental health

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