Abstract

Efficiently and objectively analyzing the complex, diverse multimodal data collected from patients at risk for dementia can be difficult in the clinical setting, contributing to high rates of underdiagnosis or misdiagnosis of this serious disorder. Patients with mild cognitive impairment (MCI) are especially at risk of developing dementia in the future. This study evaluated the ability of multi-modal machine learning (ML) methods, especially the Ensemble Integration (EI) framework, to predict future dementia development among patients with MCI. EI is a machine learning framework designed to leverage complementarity and consensus in multimodal data, which may not be adequately captured by methods used by prior dementia-related prediction studies. We tested EI's ability to predict future dementia development among MCI patients using multimodal clinical and imaging data, such as neuroanatomical measurements from structural magnetic resonance imaging (MRI) and positron emission tomography (PET) scans, from The Alzheimer's Disease Prediction of Longitudinal Evolution (TADPOLE) challenge. For predicting future dementia development among MCI patients, on a held out test set, the EI-based model performed better (AUC = 0.81, F-measure = 0.68) than the more commonly used XGBoost (AUC = 0.68, F-measure = 0.57) and deep learning (AUC = 0.79, F-measure = 0.61) approaches. This EI-based model also suggested MRI-derived volumes of regions in the middle temporal gyrus, posterior cingulate gyrus and inferior lateral ventricle brain regions to be predictive of progression to dementia.