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.

中文翻译：

---

使用基于机器学习的优化组合特征集对灰质体积和定量磁敏度映射评估和预测早期阿尔茨海默病。

背景：由于阿尔茨海默病 (AD) 的模式变化非常复杂，因此很难用特定因素对其进行评估。最近，新的机器学习方法已被应用于解决限制。

目标：本研究的目的是研究使用基于机器学习 (ML) 的优化组合特征 (OCF) 设置在灰质体积 (GMV) 和定量磁敏度映射 (QSM) 中的分类和预测方法。认知正常 (CN) 老年人、遗忘型轻度认知障碍 (aMCI) 和轻度和中度 AD 的受试者。

材料和方法：包括 57 名受试者：19 名 CN、19 名 aMCI 和 19 名具有 GMV 和 QSM 的 AD。在 AD 大脑中富含铁含量和淀粉样蛋白积累区域的众所周知的区域定义了感兴趣区域 (ROI)。为了区分三个主题组，使用 GMV 和 QSM 值进行具有三个不同内核和 OCF 集的支持向量机 (SVM)。为了预测 aMCI 阶段，使用 OCF 集执行基于回归的 ML 模型。将预测结果与临床数据的准确性进行比较。

结果：在 CN 和 aMCI 之间的组分类中，使用第二个 SVM 分类器 (AUC = 0.94) 使用 GMV（海马和内嗅皮层）和 QSM（海马和肺叶）数据的组合显示出最高准确度。在 aMCI 和 AD 之间的组分类中，使用第二个 SVM 分类器 (AUC = 0.93) 使用 GMV（杏仁核、内嗅皮层和后扣带皮层）和 QSM（海马和脑叶）数据的组合显示出最高的准确度。在 CN 和 AD 之间的组分类中，使用第二个 SVM 分类器 (AUC = 0.99) 使用 GMV（杏仁核、内嗅皮层和后扣带皮层）和 QSM（海马和枕叶）数据的组合显示出最高的准确度。从 CN 预测 aMCI，

结论：所提出的具有 GMV 和 QSM 的基于 OCF 的 ML 方法显示出对 aMCI 阶段的主题组分类和预测的有效性能。因此，它可以作为个性化分析或诊断辅助程序进行诊断。