Background:

The mobile screening test system for mild cognitive impairment (mSTS-MCI) was developed and validated to address the low sensitivity and specificity of the Montreal Cognitive Assessment (MoCA) widely used clinically.

Objective:

This study was to evaluate the efficacy machine learning algorithms based on the mSTS-MCI and Korean version of MoCA.

Method:

In total, 103 healthy individuals and 74 patients with MCI were randomly divided into training and test data sets, respectively. The algorithm using TensorFlow was trained based on the training data set, and then its accuracy was calculated based on the test data set. The cost was calculated via logistic regression in this case.

Result:

Predictive power of the algorithms was higher than those of the original tests. In particular, the algorithm based on the mSTS-MCI showed the highest positive-predictive value.

Conclusion:

The machine learning algorithms predicting MCI showed the comparable findings with the conventional screening tools.

中文翻译：

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基于筛选测试的轻度认知障碍机器学习算法。

背景：

开发并验证了轻度认知障碍移动筛查测试系统（mSTS-MCI），以解决临床广泛使用的蒙特利尔认知评估（MoCA）的低敏感性和特异性。

目的：

这项研究旨在评估基于mSTS-MCI和韩文版MoCA的机器学习算法的有效性。

方法：

总共将103例健康个体和74例MCI患者随机分为训练和测试数据集。根据训练数据集对使用TensorFlow的算法进行训练，然后根据测试数据集计算其准确性。在这种情况下，成本是通过逻辑回归计算的。

结果：

该算法的预测能力高于原始测试的能力。尤其是，基于mSTS-MCI的算法显示出最高的正预测值。

结论：

预测MCI的机器学习算法显示了与传统筛选工具相当的结果。