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Computational and Mathematical Methods in Medicine
Volume 2015, Article ID 794586, 12 pages
http://dx.doi.org/10.1155/2015/794586
Research Article

Dimensionality Reduction in Complex Medical Data: Improved Self-Adaptive Niche Genetic Algorithm

1Department of Biomedical Engineering, Zhejiang University, 38 Zheda Road, Hangzhou, Zhejiang 310027, China
2Guizhou Key Laboratory of Agricultural Bioengineering, Guizhou University, Guiyang, Guizhou 550025, China
3Zhejiang Hospital, Hangzhou, Zhejiang 310058, China

Received 24 July 2015; Revised 24 September 2015; Accepted 4 October 2015

Academic Editor: Anne Humeau-Heurtier

Copyright © 2015 Min Zhu 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.

Abstract

With the development of medical technology, more and more parameters are produced to describe the human physiological condition, forming high-dimensional clinical datasets. In clinical analysis, data are commonly utilized to establish mathematical models and carry out classification. High-dimensional clinical data will increase the complexity of classification, which is often utilized in the models, and thus reduce efficiency. The Niche Genetic Algorithm (NGA) is an excellent algorithm for dimensionality reduction. However, in the conventional NGA, the niche distance parameter is set in advance, which prevents it from adjusting to the environment. In this paper, an Improved Niche Genetic Algorithm (INGA) is introduced. It employs a self-adaptive niche-culling operation in the construction of the niche environment to improve the population diversity and prevent local optimal solutions. The INGA was verified in a stratification model for sepsis patients. The results show that, by applying INGA, the feature dimensionality of datasets was reduced from 77 to 10 and that the model achieved an accuracy of 92% in predicting 28-day death in sepsis patients, which is significantly higher than other methods.