Machine learning-based method for personalized and cost-effective detection of Alzheimer's disease

Javier Escudero, Emmanuel Ifeachor, John P Zajicek, Colin Green, James Shearer, Stephen Pearson, Alzheimer’s Disease Neuroimaging Initiative

Research output: Contribution to journalArticlepeer-review

81 Citations (Scopus)

Abstract

Diagnosis of Alzheimer's disease (AD) is often difficult, especially early in the disease process at the stage of mild cognitive impairment (MCI). Yet, it is at this stage that treatment is most likely to be effective, so there would be great advantages in improving the diagnosis process. We describe and test a machine learning approach for personalized and cost-effective diagnosis of AD. It uses locally weighted learning to tailor a classifier model to each patient and computes the sequence of biomarkers most informative or cost-effective to diagnose patients. Using ADNI data, we classified AD versus controls and MCI patients who progressed to AD within a year, against those who did not. The approach performed similarly to considering all data at once, while significantly reducing the number (and cost) of the biomarkers needed to achieve a confident diagnosis for each patient. Thus, it may contribute to a personalized and effective detection of AD, and may prove useful in clinical settings.

Original languageEnglish
Pages (from-to)164-8
Number of pages5
JournalIEEE Transactions on Biomedical Engineering
Volume60
Issue number1
DOIs
Publication statusPublished - Jan 2013

Keywords

  • Alzheimer Disease
  • Artificial Intelligence
  • Biomarkers
  • Cost-Benefit Analysis
  • Databases, Factual
  • Disease Progression
  • Female
  • Humans
  • Magnetic Resonance Imaging
  • Male
  • Mild Cognitive Impairment
  • Precision Medicine
  • Reproducibility of Results

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