Machine Learning Analysis for Quantitative Discrimination of Dried Blood Droplets
Abstract One of the most interesting and everyday natural phenomenon is the formation of different patterns after the evaporation of liquid droplets on a solid surface. The analysis of dried patterns from blood droplets has recently gained a lot of attention, experimentally and theoretically, due to...
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doaj-5201995e6a44419cb1776e4ee24716252021-02-23T09:31:58ZengNature Publishing GroupScientific Reports2045-23222020-02-0110111310.1038/s41598-020-59847-xMachine Learning Analysis for Quantitative Discrimination of Dried Blood DropletsLama Hamadeh0Samia Imran1Martin Bencsik2Graham R. Sharpe3Michael A. Johnson4David J. Fairhurst5Department of Physics and Mathematics, School of Science and Technology, Nottingham Trent UniversityDepartment of Physics and Mathematics, School of Science and Technology, Nottingham Trent UniversityDepartment of Physics and Mathematics, School of Science and Technology, Nottingham Trent UniversityExercise and Health Research Group, Sport, Health and Performance Enhancement (SHAPE) Research Centre, School of Science and Technology, Nottingham Trent UniversityExercise and Health Research Group, Sport, Health and Performance Enhancement (SHAPE) Research Centre, School of Science and Technology, Nottingham Trent UniversityDepartment of Physics and Mathematics, School of Science and Technology, Nottingham Trent UniversityAbstract One of the most interesting and everyday natural phenomenon is the formation of different patterns after the evaporation of liquid droplets on a solid surface. The analysis of dried patterns from blood droplets has recently gained a lot of attention, experimentally and theoretically, due to its potential application in diagnostic medicine and forensic science. This paper presents evidence that images of dried blood droplets have a signature revealing the exhaustion level of the person, and discloses an entirely novel approach to studying human dried blood droplet patterns. We took blood samples from 30 healthy young male volunteers before and after exhaustive exercise, which is well known to cause large changes to blood chemistry. We objectively and quantitatively analysed 1800 images of dried blood droplets, developing sophisticated image processing analysis routines and optimising a multivariate statistical machine learning algorithm. We looked for statistically relevant correlations between the patterns in the dried blood droplets and exercise-induced changes in blood chemistry. An analysis of the various measured physiological parameters was also investigated. We found that when our machine learning algorithm, which optimises a statistical model combining Principal Component Analysis (PCA) as an unsupervised learning method and Linear Discriminant Analysis (LDA) as a supervised learning method, is applied on the logarithmic power spectrum of the images, it can provide up to 95% prediction accuracy, in discriminating the physiological conditions, i.e., before or after physical exercise. This correlation is strongest when all ten images taken per volunteer per condition are averaged, rather than treated individually. Having demonstrated proof-of-principle, this method can be applied to identify diseases.https://doi.org/10.1038/s41598-020-59847-x |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Lama Hamadeh Samia Imran Martin Bencsik Graham R. Sharpe Michael A. Johnson David J. Fairhurst |
spellingShingle |
Lama Hamadeh Samia Imran Martin Bencsik Graham R. Sharpe Michael A. Johnson David J. Fairhurst Machine Learning Analysis for Quantitative Discrimination of Dried Blood Droplets Scientific Reports |
author_facet |
Lama Hamadeh Samia Imran Martin Bencsik Graham R. Sharpe Michael A. Johnson David J. Fairhurst |
author_sort |
Lama Hamadeh |
title |
Machine Learning Analysis for Quantitative Discrimination of Dried Blood Droplets |
title_short |
Machine Learning Analysis for Quantitative Discrimination of Dried Blood Droplets |
title_full |
Machine Learning Analysis for Quantitative Discrimination of Dried Blood Droplets |
title_fullStr |
Machine Learning Analysis for Quantitative Discrimination of Dried Blood Droplets |
title_full_unstemmed |
Machine Learning Analysis for Quantitative Discrimination of Dried Blood Droplets |
title_sort |
machine learning analysis for quantitative discrimination of dried blood droplets |
publisher |
Nature Publishing Group |
series |
Scientific Reports |
issn |
2045-2322 |
publishDate |
2020-02-01 |
description |
Abstract One of the most interesting and everyday natural phenomenon is the formation of different patterns after the evaporation of liquid droplets on a solid surface. The analysis of dried patterns from blood droplets has recently gained a lot of attention, experimentally and theoretically, due to its potential application in diagnostic medicine and forensic science. This paper presents evidence that images of dried blood droplets have a signature revealing the exhaustion level of the person, and discloses an entirely novel approach to studying human dried blood droplet patterns. We took blood samples from 30 healthy young male volunteers before and after exhaustive exercise, which is well known to cause large changes to blood chemistry. We objectively and quantitatively analysed 1800 images of dried blood droplets, developing sophisticated image processing analysis routines and optimising a multivariate statistical machine learning algorithm. We looked for statistically relevant correlations between the patterns in the dried blood droplets and exercise-induced changes in blood chemistry. An analysis of the various measured physiological parameters was also investigated. We found that when our machine learning algorithm, which optimises a statistical model combining Principal Component Analysis (PCA) as an unsupervised learning method and Linear Discriminant Analysis (LDA) as a supervised learning method, is applied on the logarithmic power spectrum of the images, it can provide up to 95% prediction accuracy, in discriminating the physiological conditions, i.e., before or after physical exercise. This correlation is strongest when all ten images taken per volunteer per condition are averaged, rather than treated individually. Having demonstrated proof-of-principle, this method can be applied to identify diseases. |
url |
https://doi.org/10.1038/s41598-020-59847-x |
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