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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

Robustness (%) of the algorithm.
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fig9: Robustness (%) of the algorithm.

Mentions: From Figure 9, we can see that noise poses a significant effect on the dimensionality reduction methods of PCA and MDS. In comparison with Figure 6, the accuracy of the three classifiers decreased by 18% to 35%; on the contrary, INGA is less affected by the noisy conditions, and the accuracy of the three classifiers with INGA only decreased by 3% to 13%. The robustness of the INGA algorithm is strengthened, and its antinoise ability is the best, especially when it is combined with RF.


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)

Robustness (%) of the algorithm.
© Copyright Policy
Related In: Results  -  Collection

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

fig9: Robustness (%) of the algorithm.
Mentions: From Figure 9, we can see that noise poses a significant effect on the dimensionality reduction methods of PCA and MDS. In comparison with Figure 6, the accuracy of the three classifiers decreased by 18% to 35%; on the contrary, INGA is less affected by the noisy conditions, and the accuracy of the three classifiers with INGA only decreased by 3% to 13%. The robustness of the INGA algorithm is strengthened, and its antinoise ability is the best, especially when it is combined with RF.

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