Prediction of tau accumulation in prodromal Alzheimer’s disease using an ensemble machine learning approach

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.

DEM- and GIS-Based Analysis of Soil Erosion Depth Using Machine Learning

“…Friedman [43] proposed the gradient boosting machine as a simple and highly flexible machine learning tool. It is a widely used machine learning algorithm that has been shown to be effective in a variety of applications [44][45][46]. The basic idea behind GBM is to build a prediction model using a set of poor learning algorithms, most commonly decision trees.…”

Potential Applications of Artificial Intelligence in Clinical Trials for Alzheimer’s Disease

“…However, recent findings suggested that tau pathology has more intimate links with ADrelated cognitive impairment than Aβ pathology, suggesting the tantalizing potential for clinical trials targeting tau [43,44]. A study classified 64 prodromal AD patients through GBM and RF algorithms [45]. A combination of demographic variables, MCI diagnosis information, NP test scores, APOE genotype, and cortical thickness resulted in the highest performance for GBM (AUC: 0.86) and RF (AUC: 0.82).…”

Computer-aided diagnosis and prediction in brain disorders

“…Hence, the applications of machine learning in predicting the future course of dementia include: i) predicting if a patient with cognitive impairment patient will develop dementia [137], ii) predicting when the patient will reach a clinical dementia stage (i.e. duration of the prodromal disease phase) [86], and iii) predicting the progression of biomarkers such as cognition and MRI measurements [68,63].…”