Paper Info
Title
Comparison Of A Genetic Algorithm Variable Selection And Interval Partial Least Squares For Quantitative Analysis Of Lactate In Pbs
Abstract
Blood lactate is an important biomarker that has been linked to morbidity and mortality of critically ill patients, acute ischemic stroke, septic shock, lung injuries, insulin resistance in diabetic patients, and cancer. Currently, the clinical measurement of blood lactate is done by collecting intermittent blood samples. Therefore, noninvasive, optical measurement of this significant biomarker would lead to a big leap in healthcare. This study, presents a quantitative analysis of the optical properties of lactate. The benefits of wavelength selection for the development of accurate, robust, and interpretable predictive models have been highlighted in the literature. Additionally, there is an obvious, time- and cost-saving benefit to focusing on narrower segments of the electromagnetic spectrum in practical applications. To this end, a dataset consisting of 47 spectra of Na-lactate and Phosphate Buffer Solution (PBS) was produced using a Fourier transform infrared spectrometer, and subsequently, a comparative study of the application of a genetic algorithm-based wavelength selection and two interval selection methods was carried out. The high accuracy of predictions using the developed models underlines the potential for optical measurement of lactate. Moreover, an interesting finding is the emergence of local features in the proposed genetic algorithm, while, unlike the investigated interval selection methods, no explicit constraints on the locality of features was imposed. Finally, the proposed genetic algorithm suggests the formation of a-hydroxy-esters methyl lactate in the solutions while the other investigated methods fail to indicate this.
Year
DOI
Venue
2019
10.1109/EMBC.2019.8856765
2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
Field
DocType
Volume
Methyl lactate,Computer vision,Feature selection,Pattern recognition,Computer science,Partial least squares regression,Biomarker (medicine),Artificial intelligence,Quantitative analysis (chemistry),Genetic algorithm,Phosphate buffered saline
Conference
2019
ISSN
Citations 
PageRank 
1557-170X
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Mohammadhossein Mamouei101.01
Meha Qassem200.68
Karthik Budidha301.01
Nystha Baishya401.01
Pankaj Vadgama500.34
P A Kyriacou62016.60