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
关键词:
阿尔茨海默病(AD)，轻度认知功能障碍(MCI)，定量易感性图谱(QSM)，脑灰质体积(GMV)，神经退化疾病，健忘。
摘要: 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.
