A set of 479 Mini-Mental State Examinations (MMSE) is analysed with the goal of discriminating between Alzheimer’s Disease and Vascular Dementia. The patient’s gender has been considered as a predictor in addition to answers of MMSE questions. While similar work has been previously reported, fewer patients were studied and methods inappropriate for 0/1 data were used. The study identifies entropic measures as best suited to analysing this type of data. Performance of five such methods at ordering MMSE questions by decreasing order of information contributed to diagnosis is compared. The analysis uses a novel feature selection method based on parallel estimation of conditional mutual information. The newly introduced method performs demonstrably better than classical and state of the art methods. Good predictors are temporal orientation, language recall and abstract thinking however patient gender is a stronger predictor than any of the MMSE questions.
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