Abstract
BackgroundLate-Onset Alzheimer’s Disease (LOAD) is a leading form of dementia. There is no effective cure for LOAD, leaving the treatment efforts to depend on preventive cognitive therapies, which stand to benefit from the timely estimation of the risk of developing the disease. Fortunately, a growing number of Machine Learning methods that are well positioned to address this challenge are becoming available.ResultsWe conducted systematic comparisons of representative Machine Learning models for predicting LOAD from genetic variation data provided by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. Our experimental results demonstrate that the classification performance of the best models tested yielded ∼72% of area under the ROC curve.ConclusionsMachine learning models are promising alternatives for estimating the genetic risk of LOAD. Systematic machine learning model selection also provides the opportunity to identify new genetic markers potentially associated with the disease.
Highlights
Late-Onset Alzheimer’s Disease (LOAD) is a leading form of dementia
The Bootstrap Stage-Wise Model Selection (BSWiMS), Least Absolute Shrinkage and Selection Operator (LASSO), and Recursive Partitioning and Regression Trees (RPART) had equivalent performance, and the ensemble of the methods had the best performance with a ROC score of 0.719
These plots indicate that the support vector machine (SVM) engine with minimum redundancy maximum relevance filter had the lowest performance
Summary
Late-Onset Alzheimer’s Disease (LOAD) is a leading form of dementia. There is no effective cure for LOAD, leaving the treatment efforts to depend on preventive cognitive therapies, which stand to benefit from the timely estimation of the risk of developing the disease. Alzheimer Disease (AD) is a neurodegenerative disorder that gradually destroys brain function. It is characterized by the loss of cognitive abilities such as memory, reasoning, language, and behavior. The disease leads to dementia and to death. AD is the most common form of dementia (60% – 80% cases) and occurs more often in people aged 65 and older[1].
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