A transfer learning approach based on gradient boosting machine for diagnosis of Alzheimer’s disease - Shojaie, Mehdi; Cabrerizo, Mercedes; DeKosky, Steven T.; Vaillancourt, David E.; Loewenstein, David; Duara, Ranjan; Adjouadi, Malek (Frontiers in Aging Neuroscience, 2022)
Early detection of Alzheimer's disease (AD) during the Mild Cognitive Impairment (MCI) could enable effective intervention to slow down the progression of the disease. Computer-aided diagnosis of AD relies on a sufficient amount of biomarker data. When this requirement is not fulfilled, transfer learning can be used to transfer knowledge from a source domain with more amount of labeled data than the desired target domain. In this study, an instance-based transfer learning framework is presented based on the gradient boosting machine (GBM). In GBM, a sequence of base learners is built, and each learner focuses on the errors (residuals) of the previous learner. In our transfer learning version of GBM (TrGB) a weighting mechanism based on the residuals of the base learners is defined for the source instances. Consequently, instances with different distribution than the target data will have a lower impact on the target learner. The target data in this study was obtained from the Mount Sinai dataset which is collected and processed in a collaborative 5-year project at the Mount Sinai medical center. The Alzheimer's Disease neuroimaging initiative (ADNI) dataset was used as the source domain. The experimental results showed that the proposed TrGB algorithm could improve the classification accuracy by 1.5% and 4.5% for CN vs. MCI and multiclass classifications, respectively, compared to the conventional methods. Also, using the TrGB model and transferred knowledge from the CN vs. AD classification of the source domain, the average score of early MCI vs. late MCI classification improved by 5%.