Abstract
BackgroundAssessment of risk and early diagnosis of Alzheimer's disease (AD) is a key to its prevention or slowing the progression of the disease. Previous research on risk factors for AD typically utilizes statistical comparison tests or stepwise selection with regression models. Outcomes of these methods tend to emphasize single risk factors rather than a combination of risk factors. However, a combination of factors, rather than any one alone, is likely to affect disease development. Genetic algorithms (GA) can be useful and efficient for searching a combination of variables for the best achievement (eg. accuracy of diagnosis), especially when the search space is large, complex or poorly understood, as in the case in prediction of AD development.ResultsMultiple sets of neuropsychological tests were identified by GA to best predict conversions between clinical categories, with a cross validated AUC (area under the ROC curve) of 0.90 for prediction of HC conversion to MCI/AD and 0.86 for MCI conversion to AD within 36 months.ConclusionsThis study showed the potential of GA application in the neural science area. It demonstrated that the combination of a small set of variables is superior in performance than the use of all the single significant variables in the model for prediction of progression of disease. Variables more frequently selected by GA might be more important as part of the algorithm for prediction of disease development.
Highlights
There is agreement that the identification of risk factors for Alzheimer’s disease (AD) is important for both the diagnosis and prognosis of the disease AD [1]
Variables more frequently selected by Genetic algorithms (GA) might be more important as part of the algorithm for prediction of disease development
Initial inspection of feature combinations selected by the GA showed that the partitions produced equivalent prediction result (ROC)
Summary
There is agreement that the identification of risk factors for AD is important for both the diagnosis and prognosis of the disease AD [1]. Diagnosis of AD can not be made by a single test and requires careful medical evaluation, including medical history, mental status testing, physical and neurological exam, blood test and braining imaging etc To this end there exist large prospective studies of healthy older adults at risk for AD due to their age. Prospective studies like AIBL and ADNI, typically apply multiple neuropsychological tests, each hypothesized to measure different aspects of cognitive function, to their patient groups in repeated assessments. The objective of this approach is to identify performance measures upon which impairment or its change over time indicate the presence of early AD [9,10,11]. Genetic algorithms (GA) can be useful and efficient for searching a combination of variables for the best achievement (eg. accuracy of diagnosis), especially when the search space is large, complex or poorly understood, as in the case in prediction of AD development
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