Machine-Learning Algorithms Based on Screening Tests for Mild Cognitive Impairment.

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.