Diagnose the mild cognitive impairment by constructing Bayesian network with missing data

Authors:
Yan Sun;Yiyuan Tang;Shuxue Ding;Shipin Lv;Yifen Cui
Affiliations:
Neuroinformatics Institute, Dalian University of Technology, Dalian 116024, China and Department of Computer Science, Liaoning Normal University, Dalian 116029, China and Department of Computer Sc ...;Neuroinformatics Institute, Dalian University of Technology, Dalian 116024, China;School of Computer Science and Engineering, Aizu University, Aizu-Wakamatsu City, Fukushima 965-8580, Japan;Department of Computer Science and Engineering, Dalian University of Technology, Dalian 116024, China;Neuroinformatics Institute, Dalian University of Technology, Dalian 116024, China
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

Mild Cognitive Impairment (MCI) is thought to be the prodromal phase to Alzheimer's disease (AD), which is the most common form of dementia and leads to irreversible neurogenerative damage of the brain. In order to further improve the diagnostic quality of the MCI, we developed a MCI expert system to address MCI's prediction and inference question, consequently, assist the diagnosis of doctor. In this system, we mainly deal with following problems: (1) Estimate missing data in the experiment by utilizing mutual information and Newton interpolation. (2) Make certain the prior feature ordering in constructing Bayesian network. (3) Construct the Bayesian network (We term the algorithm as MNBN). The experimental results indicate that MNBN algorithm achieved better results than some existing methods in most instances. The mean square error comes to 0.0173 in the MCI experiment. Our results shed light on the potential application in MCI diagnosis.