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

Yan Sun, Yiyuan Tang, Shuxue Ding, Shipin Lv, Yifen Cui

Research output: Contribution to journalArticlepeer-review

29 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)442-449
Number of pages8
JournalExpert Systems With Applications
Issue number1
StatePublished - Jan 2011
Externally publishedYes


  • Bayesian network
  • Mild cognitive impairment (MCI)
  • Missing data
  • Mutual information
  • Newton interpolation

ASJC Scopus subject areas

  • General Engineering
  • Computer Science Applications
  • Artificial Intelligence


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