Please use this identifier to cite or link to this item:
http://digitalrepository.fccollege.edu.pk/handle/123456789/1531
Title: | Early MCI-to-AD Conversion Prediction Using Future Value Forecasting of Multimodal Features |
Authors: | Minhas, Dr. Sidra Khanum, Dr. Aasia Alvi, Atif Riaz, Farhan Khan, Shoab A. Alsolami, Fawaz Khan, Muazzam A |
Issue Date: | 25-Jul-2021 |
Publisher: | Hindawi Computational Intelligence and Neuroscience Volume 2021, Article ID 6628036, 12 pages https://doi.org/10.1155/2021/6628036 |
Abstract: | In Alzheimer’s disease (AD) progression, it is imperative to identify the subjects with mild cognitive impairment before clinical symptoms of AD appear. )is work proposes a technique for decision support in identifying subjects who will show transition from mild cognitive impairment (MCI) to Alzheimer’s disease (AD) in the future. We used robust predictors from multivariate MRI-derived biomarkers and neuropsychological measures and tracked their longitudinal trajectories to predict signs of AD in the MCI population. Assuming piecewise linear progression of the disease, we designed a novel weighted gradient offset-based technique to forecast the future marker value using readings from at least two previous follow-up visits. Later, the complete predictor trajectories are used as features for a standard support vector machine classifier to identify MCI-to-AD progressors amongst the MCI patients enrolled in the Alzheimer’s disease neuroimaging initiative (ADNI) cohort. We explored the performance of both unimodal and multimodal models in a 5-fold cross-validation setup. )e proposed technique resulted in a high classification AUC of 91.2% and 95.7% for 6-month- and 1-year-ahead AD prediction, respectively, using multimodal markers. In the end, we discuss the efficacy of MRI markers as compared to NM for MCI-to-AD conversion prediction. |
URI: | http://localhost:8080/xmlui/handle/123456789/1531 |
Appears in Collections: | Computer Science Department |
Files in This Item:
File | Description | Size | Format | |
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6628036.pdf | 1.53 MB | Adobe PDF | View/Open |
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