Document Type
Article
Publication Date
3-8-2019
Department
Computing
School
Computing Sciences and Computer Engineering
Abstract
Introduction
Using machine learning techniques, we developed a brief questionnaire to aid neurologists and neuropsychologists in the screening of mild cognitive impairment (MCI) and dementia.
Methods
With the reduction of the survey size as a goal of this research, feature selection based on information gain was performed to rank the contribution of the 45 items corresponding to patient responses to the specified questions. The most important items were used to build the optimal screening model based on the accuracy, practicality, and interpretability. The diagnostic accuracy for discriminating normal cognition (NC), MCI, very mild dementia (VMD) and dementia was validated in the test group.
Results
The screening model (NMD-12) was constructed with the 12 items that were ranked the highest in feature selection. The receiver-operator characteristic (ROC) analysis showed that the area under the curve (AUC) in the test group was 0.94 for discriminating NC vs. MCI, 0.88 for MCI vs. VMD, 0.97 for MCI vs. dementia, and 0.96 for VMD vs. dementia, respectively.
Discussion
The NMD-12 model has been developed and validated in this study. It provides healthcare professionals with a simple and practical screening tool which accurately differentiates NC, MCI, VMD, and dementia.
Publication Title
PLoS ONE
Volume
14
Issue
3
First Page
1
Last Page
11
Recommended Citation
Chiu, P.,
Tang, H.,
Wei, C.,
Zhang, C.,
Hung, G.,
Zhou, W.
(2019). NMD-12: A New Machine-Learning Derived Screening Instrument to Detect Mild Cognitive Impairment and Dementia. PLoS ONE, 14(3), 1-11.
Available at: https://aquila.usm.edu/fac_pubs/15943
Comments
Published by 'PLoS ONE' at 10.1371/journal.pone.0213430.