Abstract
We developed machine learning (ML) algorithms to predict abnormal tau accumulation among patients with prodromal AD. We recruited 64 patients with prodromal AD using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. Supervised ML approaches based on the random forest (RF) and a gradient boosting machine (GBM) were used. The GBM resulted in an AUC of 0.61 (95% confidence interval [CI] 0.579–0.647) with clinical data (age, sex, years of education) and a higher AUC of 0.817 (95% CI 0.804–0.830) with clinical and neuropsychological data. The highest AUC was 0.86 (95% CI 0.839–0.885) achieved with additional information such as cortical thickness in clinical data and neuropsychological results. Through the analysis of the impact order of the variables in each ML classifier, cortical thickness of the parietal lobe and occipital lobe and neuropsychological tests of memory domain were found to be more important features for each classifier. Our ML algorithms predicting tau burden may provide important information for the recruitment of participants in potential clinical trials of tau targeting therapies.
Highlights
We developed machine learning (ML) algorithms to predict abnormal tau accumulation among patients with prodromal Alzheimer’s disease (AD)
We developed and compared ML approaches for prediction of the brain tau burden in prodromal AD patients using multimodal biomarkers based on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset
We found that the gradient boosting machine (GBM) with multi-model biomarkers showed a good predictive performance
Summary
We developed machine learning (ML) algorithms to predict abnormal tau accumulation among patients with prodromal AD. Unlike other ML predictions with routinely used performance measures, tree-based ML provides clinically useful information, such as the relative importance of the clinical features and whether they are related positively or negatively These interpretable ML methods have not been used for classifying tau burden in previous studies. In the combined prodromal AD and AD dementia group, increased tau PET uptake and reduced cortical thickness were associated with worse performance on a variety of neuropsychological tests[15] These biomarkers seem to be the potential features of classifiers predicting tau burdens. Variable importance and partial dependency plot (PDP) were assessed to identify the most relevant features and their relationship to tau burden
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