Literature DB >> 30119848

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

Ibrahim Olanrewaju Alade1, Aliyu Bagudu2, Tajudeen A Oyehan3, Mohd Amiruddin Abd Rahman4, Tawfik A Saleh5, Sunday Olusanya Olatunji6.   

Abstract

BACKGROUND AND OBJECTIVES: 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.
METHODS: These models utilized experimental data of wavelengths and hemoglobin concentrations in building highly accurate Genetic Algorithm/Support Vector Regression model (GA-SVR).
RESULTS: 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.
Copyright © 2018 Elsevier B.V. All rights reserved.

Keywords:  Genetic Algorithm; Hemoglobin; Refractive index; Support Vector Regression

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Year:  2018        PMID: 30119848     DOI: 10.1016/j.cmpb.2018.05.029

Source DB:  PubMed          Journal:  Comput Methods Programs Biomed        ISSN: 0169-2607            Impact factor:   5.428


  4 in total

1.  Development of a predictive model for estimating the specific heat capacity of metallic oxides/ethylene glycol-based nanofluids using support vector regression.

Authors:  Ibrahim Olanrewaju Alade; Mohd Amiruddin Abd Rahman; Aliyu Bagudu; Zulkifly Abbas; Yazid Yaakob; Tawfik A Saleh
Journal:  Heliyon       Date:  2019-06-26

2.  Predicting the Ultimate Axial Capacity of Uniaxially Loaded CFST Columns Using Multiphysics Artificial Intelligence.

Authors:  Sangeen Khan; Mohsin Ali Khan; Adeel Zafar; Muhammad Faisal Javed; Fahid Aslam; Muhammad Ali Musarat; Nikolai Ivanovich Vatin
Journal:  Materials (Basel)       Date:  2021-12-22       Impact factor: 3.623

3.  Prediction Models for Evaluating Resilient Modulus of Stabilized Aggregate Bases in Wet and Dry Alternating Environments: ANN and GEP Approaches.

Authors:  Kaffayatullah Khan; Fazal E Jalal; Mohsin Ali Khan; Babatunde Abiodun Salami; Muhammad Nasir Amin; Anas Abdulalim Alabdullah; Qazi Samiullah; Abdullah Mohammad Abu Arab; Muhammad Iftikhar Faraz; Mudassir Iqbal
Journal:  Materials (Basel)       Date:  2022-06-21       Impact factor: 3.748

4.  Refractive Index of Hemoglobin Analysis: A Comparison of Alternating Conditional Expectations and Computational Intelligence Models.

Authors:  Aida Alizamir; Amin Gholami; Nader Bahrami; Mehdi Ostadhassan
Journal:  ACS Omega       Date:  2022-09-13
  4 in total

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