Anemia detection through non-invasive analysis of lip mucosa images
Anemia detection through non-invasive analysis of lip mucosa images
This paper aims to detect anemia using images of the lip mucosa, where the skin tissue is thin, and to confirm the feasibility of detecting anemia noninvasively and in the home environment using machine learning (ML). Data were collected from 138 patients, including 100 women and 38 men. Six ML algorithms: artificial neural network (ANN), decision tree (DT), k-nearest neighbors (KNN), logistic regression (LR), naive bayes (NB), and support vector machine (SVM) which are widely used in medical applications, were used to classify the collected data. Two different data types were obtained from participants' images (RGB red color values and HSV saturation values) as features, with age, sex, and hemoglobin levels utilized to perform classification. The ML algorithm was used to analyze and classify images of the lip mucosa quickly and accurately, potentially increasing the efficiency of anemia screening programs. The accuracy, precision, recall, and F-measure were evaluated to assess how well ML models performed in predicting anemia. The results showed that NB reported the highest accuracy (96%) among the other ML models used. DT, KNN and ANN reported an accuracies of (93%), while LR and SVM had an accuracy of (79%) and (75%) receptively. This research suggests that employing ML approaches to identify anemia will help classify the diagnosis, which will then help to create efficient preventive measures. Compared to blood tests, this noninvasive procedure is more practical and accessible to patients. Furthermore, ML algorithms may be created and trained to assess lip mucosa photos at a minimal cost, making it an affordable screening method in regions with a shortage of healthcare resources.
anemia, classification, decision tree, machine learning, support vector machine (SVM)
Donmez, Tuurker Berk
1233011f-8971-496b-8e83-8eeb9ffa1d39
Mansour, Mohammed
23071427-e171-4a2f-9bb7-4aa6db8b5c65
Kutlu, Mustafa
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Freeman, Chris
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Mahmud, Shekhar
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2023
Donmez, Tuurker Berk
1233011f-8971-496b-8e83-8eeb9ffa1d39
Mansour, Mohammed
23071427-e171-4a2f-9bb7-4aa6db8b5c65
Kutlu, Mustafa
f0592223-1cb3-493b-8034-a2e07e3e9f3b
Freeman, Chris
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
Mahmud, Shekhar
7a12f097-7ca0-438c-98ce-0588de342410
Donmez, Tuurker Berk, Mansour, Mohammed, Kutlu, Mustafa, Freeman, Chris and Mahmud, Shekhar
(2023)
Anemia detection through non-invasive analysis of lip mucosa images.
Frontiers in Big Data, 6, [1241899].
(doi:10.3389/fdata.2023.1241899).
Abstract
This paper aims to detect anemia using images of the lip mucosa, where the skin tissue is thin, and to confirm the feasibility of detecting anemia noninvasively and in the home environment using machine learning (ML). Data were collected from 138 patients, including 100 women and 38 men. Six ML algorithms: artificial neural network (ANN), decision tree (DT), k-nearest neighbors (KNN), logistic regression (LR), naive bayes (NB), and support vector machine (SVM) which are widely used in medical applications, were used to classify the collected data. Two different data types were obtained from participants' images (RGB red color values and HSV saturation values) as features, with age, sex, and hemoglobin levels utilized to perform classification. The ML algorithm was used to analyze and classify images of the lip mucosa quickly and accurately, potentially increasing the efficiency of anemia screening programs. The accuracy, precision, recall, and F-measure were evaluated to assess how well ML models performed in predicting anemia. The results showed that NB reported the highest accuracy (96%) among the other ML models used. DT, KNN and ANN reported an accuracies of (93%), while LR and SVM had an accuracy of (79%) and (75%) receptively. This research suggests that employing ML approaches to identify anemia will help classify the diagnosis, which will then help to create efficient preventive measures. Compared to blood tests, this noninvasive procedure is more practical and accessible to patients. Furthermore, ML algorithms may be created and trained to assess lip mucosa photos at a minimal cost, making it an affordable screening method in regions with a shortage of healthcare resources.
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1241899_Manuscript-2
- Accepted Manuscript
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fdata-06-1241899
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More information
Accepted/In Press date: 3 October 2023
e-pub ahead of print date: 19 October 2023
Published date: 2023
Additional Information:
A correction has been attached to this output located at https://www.frontiersin.org/articles/10.3389/fdata.2023.1335213/full and http://doi.org/10.3389/fdata.2023.1335213
Funding Information:
The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.
Publisher Copyright:
Copyright © 2023 Donmez, Mansour, Kutlu, Freeman and Mahmud.
Keywords:
anemia, classification, decision tree, machine learning, support vector machine (SVM)
Identifiers
Local EPrints ID: 485007
URI: http://eprints.soton.ac.uk/id/eprint/485007
ISSN: 2624-909X
PURE UUID: abc8e90e-b9d1-488d-82ec-d899a8e05e7b
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Date deposited: 28 Nov 2023 17:34
Last modified: 11 Dec 2024 02:39
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Contributors
Author:
Tuurker Berk Donmez
Author:
Mohammed Mansour
Author:
Mustafa Kutlu
Author:
Chris Freeman
Author:
Shekhar Mahmud
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