Detecting very early stages of dementia from normal aging with Machine Learning methods

  • William Rodman Shankle
  • Subramani Mani
  • Michael J. Pazzani
  • Padhraic Smyth
Knowledge Acquisition and Learning
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1211)


We used Machine Learning (ML) methods to learn the best decision rules to distinguish normal brain aging from the earliest stages of dementia using subsamples of 198 normal and 244 cognitively impaired or very mildly demented (Clinical Dementia Rating Scale=0.5) persons. Subjects were represented by their age, education and gender, plus their responses on the Functional Activities Questionnaire (FAQ), the Mini-Mental Status Exam (MMSE), and the Ishihara Color Plate (ICP) tasks. The ML algorithms applied to these data contained within the electronic patient records of a medical relational database, learned rule sets that were as good as or better than any rules derived from either the literature or from domain specific knowledge provided by expert clinicians. All ML algorithms for all runs found that a single question from the FAQ, the forgetting rule, (“Do you require assistance remembering appointments, family occasions, holidays, or taking medications?”) was the only attribute included in all rule sets. CART's tree simplification procedure always found that just the forgetting rule gave the best pruned decision tree rule set with classification accuracy (93% sensitivity and 80% specificity) as high as or better than any other decision tree rule-set. Comparison with published classification accuracies for the FAQ and MMSE revealed that including some of the additional attributes in these tests actually worsen classification accuracy. Stepwise logistic regression using the FAQ attributes to classify dementia status confirmed that the forgetting rule gave a much larger odds ratio than any other attribute and was the only attribute included in all of the stepwise logistic regressions performed on 33 random samples of the data. Stepwise logistic regression using the MMSE attributes identified two attributes which occurred in all 33 runs and had by far the highest odds ratio. In summary, ML methods have discovered that the simplest and most sensitive screening test for the earliest clinical stages of dementia consists of a single question, the forgetting rule.


Stepwise Logistic Regression Naive Bayesian Classifier Clinical Dementia Rate Scale Demented Subject Dementia Status 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • William Rodman Shankle
    • 1
  • Subramani Mani
    • 2
  • Michael J. Pazzani
    • 2
  • Padhraic Smyth
    • 2
  1. 1.Departments of Neurology and Information and Computer ScienceUniversity of California at IrvineIrvine
  2. 2.Department of Information and Computer ScienceUniversity of California at IrvineIrvine

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