Comparison of Dimensionality Reduction Methods for Multimodal Classification of Early Stages of Alzheimer's Disease

• Published in: Proceedings (International Conference on Computational Science and Computational Intelligence) pp. 1598 - 1603
• Main Authors: Sawada, Luana Okino, Morar, Ulyana, Mayrand, Robin Perry, Freytes, Christian Yaphet, Adeyosoye, Micheal, Cabrerizo, Mercedes, Curiel Cid, Rosie E., Loewenstein, David, Duara, Ranjan, Adjouadi, Malek
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2022
• Subjects: Alzheimer's disease; Analysis of variance; Bagging; Classification; Dimensionality reduction; Machine Learning; Magnetic resonance imaging; Scientific computing; Visualization
• ISSN: 2769-5654
• DOI: 10.1109/CSCI58124.2022.00286

Early diagnosis of Alzheimer's Disease (AD) is challenging due to its progressive nature. This study proposes a comprehensive comparison of four classifiers combined with different dimensionality reduction methods to discriminate normal controls (CN) from pre-mild cognitive impairment (pMCI) and early MCI (EMCI) using multimodal datasets including MRIs, PETs, SUVr, clinician amyloid visual reads, and subjects demographics. The most robust classifier for CN vs. MCI is the Mutual Information Best Percentile - Bagging Classifier combination, with 73.91% accuracy and a 4.82% standard deviation (SD). The best performance of 65.23% (11.84% SD) accuracy for CN vs. EMCI was DTC with ANOVA. In comparing CN with pMCI the best classification accuracy was ANOVA-DTC 51.06% (14.19% SD). An accuracy of 56.34% (10.67% SD) was achieved by bagging with ANOVA for multiclass classification of CN vs. pMCI vs. EMCI.