Document Type
Article
Publication Date
3-7-2020
Department
Department of Computer Science
Abstract
Objective. The reliable diagnosis remains a challenging issue in the early stages of dementia. We aimed to develop and validate a new method based on machine learning to help the preliminary diagnosis of normal, mild cognitive impairment (MCI), very mild dementia (VMD), and dementia using an informant-based questionnaire. Methods. We enrolled 5,272 individuals who filled out a 37-item questionnaire. In order to select the most important features, three different techniques of feature selection were tested. Then, the top features combined with six classification algorithms were used to develop the diagnostic models. Results. Information Gain was the most effective among the three feature selection methods. The Naive Bayes algorithm performed the best (accuracy = 0.81, precision = 0.82, recall = 0.81, and F-measure = 0.81) among the six classification models. Conclusion. The diagnostic model proposed in this paper provides a powerful tool for clinicians to diagnose the early stages of dementia.
Publication Title
Scientific Programming
Recommended Citation
Zhu, F.,
Li, X.,
Tang, H.,
He, Z.,
Zhang, C.,
Hung, G.,
Chiu, P.,
&
Zhou, W.
(2020).
Machine learning for the preliminary diagnosis of dementia.
Scientific Programming,
2020.
http://doi.org/10.1155/2020/5629090
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/1815
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Version
Publisher's PDF
Publisher's Statement
Copyright © 2020 Fubao Zhu et al. this is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Publisher’s version of record: https://doi.org/10.1155/2020/5629090