Prediction of cognitive impairment via deep learning trained with multi-center neuropsychological test data

Min Ju Kang, Sang Yun Kim, Duk L. Na, Byeong C. Kim, Dong Won Yang, Eun Joo Kim, Hae Ri Na, Hyun Jeong Han, Jae Hong Lee, Jong Hun Kim, Kee Hyung Park, Kyung Won Park, Seol Heui Han, Seong Yoon Kim, Soo Jin Yoon, Bora Yoon, Sang Won Seo, So Young Moon, Youngsoon Yang, Yong S. ShimMin Jae Baek, Jee Hyang Jeong, Seong Hye Choi, Young Chul Youn

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39 Scopus citations

Abstract

Background: Neuropsychological tests (NPTs) are important tools for informing diagnoses of cognitive impairment (CI). However, interpreting NPTs requires specialists and is thus time-consuming. To streamline the application of NPTs in clinical settings, we developed and evaluated the accuracy of a machine learning algorithm using multi-center NPT data. Methods: Multi-center data were obtained from 14,926 formal neuropsychological assessments (Seoul Neuropsychological Screening Battery), which were classified into normal cognition (NC), mild cognitive impairment (MCI) and Alzheimer's disease dementia (ADD). We trained a machine learning model with artificial neural network algorithm using TensorFlow (https://www.tensorflow.org) to distinguish cognitive state with the 46-variable data and measured prediction accuracies from 10 randomly selected datasets. The features of the NPT were listed in order of their contribution to the outcome using Recursive Feature Elimination. Results: The ten times mean accuracies of identifying CI (MCI and ADD) achieved by 96.66 ± 0.52% of the balanced dataset and 97.23 ± 0.32% of the clinic-based dataset, and the accuracies for predicting cognitive states (NC, MCI or ADD) were 95.49 ± 0.53 and 96.34 ± 1.03%. The sensitivity to the detection CI and MCI in the balanced dataset were 96.0 and 96.0%, and the specificity were 96.8 and 97.4%, respectively. The 'time orientation' and '3-word recall' score of MMSE were highly ranked features in predicting CI and cognitive state. The twelve features reduced from 46 variable of NPTs with age and education had contributed to more than 90% accuracy in predicting cognitive impairment. Conclusions: The machine learning algorithm for NPTs has suggested potential use as a reference in differentiating cognitive impairment in the clinical setting.

Original languageEnglish
Article number231
JournalBMC Medical Informatics and Decision Making
Volume19
Issue number1
DOIs
StatePublished - 21 Nov 2019

Bibliographical note

Funding Information:
of the Republic of Korea and the National Research Foundation of Korea (NRF-2017S1A6A3A01078538), Korea Ministry of Health & Welfare, and from the Original Technology Research Program for Brain Science through the National Research Foundation of Korea funded by the Korean Government (MSIP; No. 2014M3C7A1064752).

Funding Information:
The publication costs, design of the study, data management and writing the manuscript for this article were supported by the Ministry of Education

Publisher Copyright:
© 2019 The Author(s).

Keywords

  • Alzheimer's disease
  • Dementia
  • Machine learning
  • Mild cognitive impairment
  • Neuropsychological test

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