Predicting Rate of Cognitive Decline at Baseline Using a Deep Neural Network with Multidata Analysis
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This study investigates whether a machine-learning-based system can predict the rate of cognitive-decline in mildly cognitively impaired (MCI) patients by processing only the clinical and imaging data collected at the initial visit. We build a predictive model based on a supervised hybrid neural network utilizing a 3-Dimensional Convolutional Neural Network to perform volume analysis of Magnetic Resonance Imaging (MRI) and integration of non-imaging clinical data at the fully connected layer of the architecture. The analysis is performed on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Experimental results confirm that there is a correlation between cognitive decline and the data obtained at the first visit. The system achieved an area under the receiver operator curve (AUC) of 66.6 prediction.
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