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The transition from mild cognitive impairment (MCI) to Alzheimer's disease(AD) is of great interest to clinical researchers. This phenomenon also servesas a valuable data source for quantitative methodological researchersdeveloping new approaches for classification. However, the growth of machinelearning (ML) approaches for classification may falsely lead many clinicalresearchers to underestimate the value of logistic regression (LR), yieldingequivalent or superior classification accuracy over other ML methods. Further,in applications with many features that could be used for classifying thetransition, clinical researchers are often unaware of the relative value ofdifferent selection procedures. In the present study, we sought to investigatethe use of automated and theoretically-guided feature selection techniques, andas well as the L-1 norm when applying different classification techniques forpredicting conversion from MCI to AD in a highly characterized and studiedsample from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We proposean alternative pre-selection technique that utilizes an efficient featureselection based on clinical knowledge of brain regions involved in AD. Thepresent findings demonstrate how similar performance can be achieved usinguser-guided pre-selection versus algorithmic feature selection techniques.Finally, we compare the performance of a support vector machine (SVM) with thatof logistic regression on multi-modal data from ADNI. The present findings showthat although SVM and other ML techniques are capable of relatively accurateclassification, similar or higher accuracy can often be achieved by LR,mitigating SVM's necessity or value for many clinical researchers.

A Role for Prior Knowledge in Statistical Classification of the Transition from MCI to Alzheimer's Disease