Department of Occupational Therapy, College of Medical Science, Soonchunhyang University, Asan, Korea.
Am J Alzheimers Dis Other Demen. 2020 Jan-Dec;35:1533317520927163. doi: 10.1177/1533317520927163.
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.
This study was to evaluate the efficacy machine learning algorithms based on the mSTS-MCI and Korean version of MoCA.
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.
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.
The machine learning algorithms predicting MCI showed the comparable findings with the conventional screening tools.
为了解决广泛应用于临床的蒙特利尔认知评估（MoCA）敏感性和特异性低的问题，开发并验证了轻度认知障碍（MCI）的移动筛查测试系统（mSTS-MCI）。
本研究旨在评估基于 mSTS-MCI 和韩国版 MoCA 的机器学习算法的效果。
共纳入 103 名健康个体和 74 名 MCI 患者，将其随机分为训练集和测试数据集。基于训练数据集，使用 TensorFlow 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，通过逻辑回归计算代价。
算法的预测能力高于原始测试。特别是，基于 mSTS-MCI 的算法显示出最高的阳性预测值。
预测 MCI 的机器学习算法与传统的筛查工具具有可比的发现。
