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

Zhu M, Xia J, Yan M, Cai G, Yan J, Ning G - Comput Math Methods Med (2015)

Bottom Line: High-dimensional clinical data will increase the complexity of classification, which is often utilized in the models, and thus reduce efficiency.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Engineering, Zhejiang University, 38 Zheda Road, Hangzhou, Zhejiang 310027, China ; Guizhou Key Laboratory of Agricultural Bioengineering, Guizhou University, Guiyang, Guizhou 550025, China.

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.

No MeSH data available.


Related in: MedlinePlus

The relationship between encoding and features.
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fig3: The relationship between encoding and features.

Mentions: Set the Encoding. The data are organized in a table with 77 columns for attributes of patients and each bit is assigned to one feature; thus the encoding length is designed as 77. If the ith bit equals 1, then the ith feature is involved in classification; otherwise, the corresponding feature is not involved, as shown in Figure 3.


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

Zhu M, Xia J, Yan M, Cai G, Yan J, Ning G - Comput Math Methods Med (2015)

The relationship between encoding and features.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4663319&req=5

fig3: The relationship between encoding and features.
Mentions: Set the Encoding. The data are organized in a table with 77 columns for attributes of patients and each bit is assigned to one feature; thus the encoding length is designed as 77. If the ith bit equals 1, then the ith feature is involved in classification; otherwise, the corresponding feature is not involved, as shown in Figure 3.

Bottom Line: High-dimensional clinical data will increase the complexity of classification, which is often utilized in the models, and thus reduce efficiency.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Engineering, Zhejiang University, 38 Zheda Road, Hangzhou, Zhejiang 310027, China ; Guizhou Key Laboratory of Agricultural Bioengineering, Guizhou University, Guiyang, Guizhou 550025, China.

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.

No MeSH data available.


Related in: MedlinePlus