Detecting Very Early Stages of Dementia from Normal Aging with Machine Learning Methods

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 MiniMental 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 specic 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 inc...

• Type: Conference
• Year: 1997
• Where: AIME
• Authors: William Rodman Shankle, Subramani Mani, Michael J. Pazzani, Padhraic Smyth
• Keywords: AIME 1997, Artificial Intelligence, Classi Cation, Rule Set, Stepwise Logistic Regression