Machine-learning techniques for building a diagnostic model for very mild dementia

Neuroimage. 2010 Aug 1;52(1):234-44. doi: 10.1016/j.neuroimage.2010.03.084. Epub 2010 Apr 9.


Many researchers have sought to construct diagnostic models to differentiate individuals with very mild dementia (VMD) from healthy elderly people, based on structural magnetic-resonance (MR) images. These models have, for the most part, been based on discriminant analysis or logistic regression, with few reports of alternative approaches. To determine the relative strengths of different approaches to analyzing structural MR data to distinguish people with VMD from normal elderly control subjects, we evaluated seven different classification approaches, each of which we used to generate a diagnostic model from a training data set acquired from 83 subjects (33 VMD and 50 control). We then evaluated each diagnostic model using an independent data set acquired from 30 subjects (13 VMD and 17 controls). We found that there were significant performance differences across these seven diagnostic models. Relative to the diagnostic models generated by discriminant analysis and logistic regression, the diagnostic models generated by other high-performance diagnostic-model-generation algorithms manifested increased generalizability when diagnostic models were generated from all atlas structures.

Publication types

  • Comparative Study
  • Evaluation Study
  • Research Support, American Recovery and Reinvestment Act
  • Research Support, N.I.H., Extramural

MeSH terms

  • Aged
  • Algorithms
  • Artificial Intelligence*
  • Atlases as Topic
  • Brain / pathology*
  • Databases as Topic
  • Dementia / diagnosis*
  • Dementia / pathology*
  • Diagnosis, Computer-Assisted / methods*
  • Discriminant Analysis
  • Female
  • Humans
  • Image Processing, Computer-Assisted / methods
  • Logistic Models
  • Magnetic Resonance Imaging
  • Male
  • Models, Neurological*
  • Sensitivity and Specificity
  • Temporal Lobe / pathology