INTRODUCTION: Reducing the amount of testing required to accurately detect @entity73 is clinically relevant. The aim of this research was to determine the fewest number of clinical measures required to accurately classify @entity1 as healthy older adult, @entity73 ( @entity73 ), or @entity303 using a suite of classification techniques. METHOD: Two variable selection machine learning models (i.e., naive Bayes, decision tree), a logistic regression, and two @entity1 datasets (i.e., clinical diagnosis; Clinical @entity303 Rating, @entity7958 ) were explored. @entity1 classified using clinical diagnosis criteria included 52 individuals with @entity303 , 97 with @entity73 , and 161 cognitively healthy older adults. @entity1 classified using @entity7958 included 154 individuals with @entity7958 = 0, 93 individuals with @entity7958 = 0.5, and 25 individuals with @entity7958 = 1.0+. A total of 27 demographic, psychological, and neuropsychological variables were available for variable selection. RESULTS: No significant difference was observed between naive Bayes, decision tree, and logistic regression models for classification of both clinical diagnosis and @entity7958 datasets. @entity1 classification (70.0-99.1%), geometric mean (60.9-98.1%), sensitivity (44.2-100%), and specificity (52.7-100%) were generally satisfactory. Unsurprisingly, the @entity73 / @entity7958 = 0.5 @entity1 group was the most challenging to classify. Through variable selection only 2-9 variables were required for classification and varied between datasets in a clinically meaningful way. CONCLUSIONS: The current study results reveal that machine learning techniques can accurately classify @entity73 and reduce the number of measures required for diagnosis.

Neuropsychological test selection for XXXX classification: A machine learning approach.

@entity1 :: ('9606', 'Species') :: ['participants', 'participant', 'Participant', 'Participants'] @entity73 :: ('MESH:D003072', 'Disease') :: ['cognitive impairment', 'MCI', 'mild cognitive impairment'] @entity303 :: ('MESH:D003704', 'Disease') :: ['Dementia', 'dementia'] @entity7958 :: ('1038', 'Gene') :: ['CDR']
@entity73 :: (MESH:D003072,Disease) :: ['cognitive impairment']
