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Estimating the refractive index of oxygenated and deoxygenated hemoglobin using genetic algorithm – support vector regression model

Overview of attention for article published in Computer Methods & Programs in Biomedicine, May 2018
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Title
Estimating the refractive index of oxygenated and deoxygenated hemoglobin using genetic algorithm – support vector regression model
Published in
Computer Methods & Programs in Biomedicine, May 2018
DOI 10.1016/j.cmpb.2018.05.029
Pubmed ID
Authors

Ibrahim Olanrewaju Alade, Aliyu Bagudu, Tajudeen A Oyehan, Mohd Amiruddin Abd Rahman, Tawfik A Saleh, Sunday Olusanya Olatunji

Abstract

The refractive index of hemoglobin plays important role in hematology due to its strong correlation with the pathophysiology of different diseases. Measurement of the real part of the refractive index remains a challenge due to strong absorption of the hemoglobin especially at relevant high physiological concentrations. So far, only a few studies on direct measurement of refractive index have been reported and there are no firm agreements on the reported values of refractive index of hemoglobin due to measurement artifacts. In addition, it is time consuming, laborious and expensive to perform several experiments to obtain the refractive index of hemoglobin. In this work, we proposed a very rapid and accurate computational intelligent approach using Genetic Algorithm/Support Vector Regression models to estimate the real part of the refractive index for oxygenated and deoxygenated hemoglobin samples. These models utilized experimental data of wavelengths and hemoglobin concentrations in building highly accurate Genetic Algorithm/Support Vector Regression model (GA-SVR). The developed methodology showed high accuracy as indicated by the low root mean square error values of 4.65 × 10-4 and 4.62 × 10-4 for oxygenated and deoxygenated hemoglobin, respectively. In addition, the models exhibited 99.85 and 99.84% correlation coefficients (r) for the oxygenated and deoxygenated hemoglobin, thus, validating the strong agreement between the predicted and the experimental results CONCLUSIONS: Due to the accuracy and relative simplicity of the proposed models, we envisage that these models would serve as important references for future studies on optical properties of blood.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 49 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 49 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 9 18%
Student > Ph. D. Student 6 12%
Other 4 8%
Student > Bachelor 4 8%
Researcher 3 6%
Other 6 12%
Unknown 17 35%
Readers by discipline Count As %
Engineering 10 20%
Physics and Astronomy 5 10%
Computer Science 4 8%
Environmental Science 2 4%
Chemical Engineering 2 4%
Other 3 6%
Unknown 23 47%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 15 August 2018.
All research outputs
#22,767,715
of 25,382,440 outputs
Outputs from Computer Methods & Programs in Biomedicine
#1,664
of 2,059 outputs
Outputs of similar age
#302,279
of 344,093 outputs
Outputs of similar age from Computer Methods & Programs in Biomedicine
#28
of 35 outputs
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