Evaluation and Prediction of Early Alzheimer’s Disease Using a Machine Learning-based Optimized Combination-Feature Set on Gray Matter Volume and Quantitative Susceptibility Mapping

Title:Evaluation and Prediction of Early Alzheimer’s Disease Using a Machine Learning-based Optimized Combination-Feature Set on Gray Matter Volume and Quantitative Susceptibility Mapping

Volume: 17 Issue: 5

Author(s): Hyug-Gi Kim, Soonchan Park, Hak Y. Rhee, Kyung M. Lee, Chang-Woo Ryu, Soo Y. Lee, Eui J. Kim, Yi Wang and Geon-Ho Jahng*

Affiliation:

• Department of Radiology, Kyung Hee University Hospital at Gangdong, College of Medicine, Kyung Hee University 892 Dongnam-ro, Gangdong-Gu, Seoul-05278,Korea

Keywords: Alzheimer`s disease (AD), mild cognitive impairment (MCI), quantitative susceptibility mapping (QSM), gray matter volume (GMV), neurodegenerative disorder, memory loss.

Abstract: Background: Because Alzheimer’s Disease (AD) has very complicated pattern changes, it is difficult to evaluate it with a specific factor. Recently, novel machine learning methods have been applied to solve limitations.

Objective: The objective of this study was to investigate the approach of classification and prediction methods using the Machine Learning (ML)-based Optimized Combination-Feature (OCF) set on Gray Matter Volume (GMV) and Quantitative Susceptibility Mapping (QSM) in the subjects of Cognitive Normal (CN) elderly, Amnestic Mild Cognitive Impairment (aMCI), and mild and moderate AD.

Materials and Methods: 57 subjects were included: 19 CN, 19 aMCI, and 19 AD with GMV and QSM. Regions-of-Interest (ROIs) were defined at the well-known regions for rich iron contents and amyloid accumulation areas in the AD brain. To differentiate the three subject groups, the Support Vector Machine (SVM) with the three different kernels and with the OCF set was conducted with GMV and QSM values. To predict the aMCI stage, regression-based ML models were performed with the OCF set. The result of prediction was compared with the accuracy of clinical data.

Results: In the group classification between CN and aMCI, the highest accuracy was shown using the combination of GMVs (hippocampus and entorhinal cortex) and QSMs (hippocampus and pulvinar) data using the 2nd SVM classifier (AUC = 0.94). In the group classification between aMCI and AD, the highest accuracy was shown using the combination of GMVs (amygdala, entorhinal cortex, and posterior cingulate cortex) and QSMs (hippocampus and pulvinar) data using the 2nd SVM classifier (AUC = 0.93). In the group classification between CN and AD, the highest accuracy was shown using the combination of GMVs (amygdala, entorhinal cortex, and posterior cingulate cortex) and QSMs (hippocampus and pulvinar) data using the 2nd SVM classifier (AUC = 0.99). To predict aMCI from CN, the exponential Gaussian process regression model with the OCF set using GMV and QSM data was shown the most similar result (RMSE = 0.371) to clinical data (RMSE = 0.319).

Conclusion: The proposed OCF based ML approach with GMV and QSM was shown the effective performance of the subject group classification and prediction for aMCI stage. Therefore, it can be used as personalized analysis or diagnostic aid program for diagnosis.

Cite this article as:

Kim Hyug-Gi , Park Soonchan , Rhee Y. Hak , Lee M. Kyung , Ryu Chang-Woo , Lee Y. Soo , Kim J. Eui , Wang Yi and Jahng Geon-Ho *, Evaluation and Prediction of Early Alzheimer’s Disease Using a Machine Learning-based Optimized Combination-Feature Set on Gray Matter Volume and Quantitative Susceptibility Mapping, Current Alzheimer Research 2020; 17 (5) . https://dx.doi.org/10.2174/1567205017666200624204427

DOI https://dx.doi.org/10.2174/1567205017666200624204427	Print ISSN 1567-2050
Publisher Name Bentham Science Publisher	Online ISSN 1875-5828