Multimodal ensemble model for Alzheimer's disease conversion prediction from Early Mild Cognitive Impairment subjects

• Published in: Computers in biology and medicine Vol. 151; no. Pt A; p. 106201
• Main Authors: Velazquez, Matthew, Lee, Yugyung
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.12.2022; Elsevier Limited
• Subjects: Accuracy; Algorithms; Alzheimer Disease - diagnostic imaging; Alzheimer's disease; Artificial neural networks; Biomarkers; Classification; Classifiers; Cognitive ability; Cognitive Dysfunction - diagnostic imaging; Conversion; Convolutional neural network; Deep learning; Dementia disorders; Diffusion; Diffusion Tensor Imaging; Electronic health records; Electronic medical records; Ensemble model with grid search; Explainability; Feature selection; Humans; Impairment; Internal Medicine; Longitudinal studies; Machine learning; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Mathematical analysis; Medical imaging; Mild cognitive impairment; Mortality; Neural networks; Neurodegenerative diseases; Neuroimaging; Neuroimaging - methods; Neuropsychology; Other; Patients; Predictions; Random forest; Search algorithms; Support vector machines; Tensors; Mild cognitive impairment; Convolutional neural network; Machine learning; Random forest; Longitudinal studies; Electronic health records; Explainability; Alzheimer's disease; Ensemble model with grid search; Diffusion tensor imaging
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2022.106201

Alzheimer's Disease (AD) is the most common type of dementia. Predicting the conversion to Alzheimer's from the mild cognitive impairment (MCI) stage is a complex problem that has been studied extensively. This study centers on individualized EMCI (the earliest MCI subset) to AD conversion prediction on multimodal data such as diffusion tensor imaging (DTI) scans and electronic health records (EHR) for their patients using the combination of both a balanced random forest model alongside a convolutional neural network (CNN) model. Our random forest model leverages EHR's patient biometric and neuropsychiatric test score features, while our CNN model uses the patient's diffusion tensor imaging (DTI) scans for conversion prediction. To accomplish this, 383 Early Mild Cognitive Impairment (EMCI) patients were collected from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Within this set, 49 patients would eventually convert to AD (EMCI_C), whereas the remaining 335 did not convert (EMCI_NC). For the EHR-based classifier, 288 patients were used to train the random forest model, with 95 set aside for testing. For the CNN classifier, 405 DTI images were collected across 90 distinct patients. Nine clinical features were selected to be combined with the visual predictor. Due to the imbalanced classes, oversampling was performed for the clinical features and augmentation for the DTI images. A grid search algorithm is also used to determine the ideal weighting between our two models. Our results indicate that an ensemble model was effective (98.81% accuracy) at EMCI to AD conversion prediction. Additionally, our ensemble model provides explainability as feature importance can be assessed at both the model and individual prediction levels. Therefore, this ensemble model could serve as a diagnostic support tool or a means for identifying clinical trial candidates. •Prediction of conversion from mild cognitive impairment (MCI) to Alzheimer's Disease (AD).•Random forest-based AD conversion prediction leveraging Electronic Health Records (EHR).•Grid Search-based Ensemble model of CNN and Random Forest as a means for a diagnostic support tool.•Explainability with feature importance determined by model and individual prediction levels.•The predictive superiority of the proposed ensemble model over the state-of-the-art models.