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

Computational and Mathematical Methods in Medicine. 2015;2015 DOI 10.1155/2015/258761

 

Journal Homepage

Journal Title: Computational and Mathematical Methods in Medicine

ISSN: 1748-670X (Print); 1748-6718 (Online)

Publisher: Hindawi Limited

LCC Subject Category: Medicine: Medicine (General): Computer applications to medicine. Medical informatics

Country of publisher: United Kingdom

Language of fulltext: English

Full-text formats available: PDF, HTML, ePUB, XML

 

AUTHORS

Ziming Yin (College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang 31002, China)
Yinhong Zhao (China National Center for Biotechnology Development, Building D, No. 16, Xisihuanzhonglu, Haidian District, Beijing 100036, China)
Xudong Lu (College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang 31002, China)
Huilong Duan (College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang 31002, China)

EDITORIAL INFORMATION

Blind peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 24 weeks

 

Abstract | Full Text

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.