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Computational and Mathematical Methods in Medicine
Volume 2015, Article ID 258761, 13 pages
Research Article

A Hybrid Intelligent Diagnosis Approach for Quick Screening of Alzheimer’s Disease Based on Multiple Neuropsychological Rating Scales

1College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang 31002, China
2China National Center for Biotechnology Development, Building D, No. 16, Xisihuanzhonglu, Haidian District, Beijing 100036, China

Received 6 June 2014; Revised 20 November 2014; Accepted 20 November 2014

Academic Editor: José M. Jerez

Copyright © 2015 Ziming Yin 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.


Neuropsychological testing is an effective means for the screening of Alzheimer’s disease. Multiple neuropsychological rating scales should be used together to get subjects’ comprehensive cognitive state due to the limitation of a single scale, but it is difficult to operate in primary clinical settings because of the inadequacy of time and qualified clinicians. Aiming at identifying AD’s stages more accurately and conveniently in screening, we proposed a computer-aided diagnosis approach based on critical items extracted from multiple neuropsychological scales. The proposed hybrid intelligent approach combines the strengths of rough sets, genetic algorithm, and Bayesian network. There are two stages: one is attributes reduction technique based on rough sets and genetic algorithm, which can find out the most discriminative items for AD diagnosis in scales; the other is uncertain reasoning technique based on Bayesian network, which can forecast the probability of suffering from AD. The experimental data set consists of 500 cases collected by a top hospital in China and each case is determined by the expert panel. The results showed that the proposed approach could not only reduce items drastically with the same classification precision, but also perform better on identifying different stages of AD comparing with other existing scales.