Machine Learning for the Preliminary Diagnosis of Dementia

Authors

Fubao Zhu, Zhengzhou University of Light Industry
Xiaonan Li, Zhengzhou University of Light IndustryFollow
Haipeng Tang, University of Southern MississippiFollow
Zhuo He, Michigan Technological UniversityFollow
Chaoyang Zhang, University of Southern MississippiFollow
Guang-Uei Hung, Chang Bing Show Chwan Memorial HospitalFollow
Pai-Yi Chiu, Show Chwan Memorial Hospital
Weihua Zhou, Michigan Technological UniversityFollow

Document Type

Article

Publication Date

3-7-2020

School

Computing Sciences and Computer Engineering

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.

Comments

Published by 'Scientific Programming' at 10.1155/2020/5629090.

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.
Available at: https://aquila.usm.edu/fac_pubs/17152