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Optimizing Anemia Detection Using Effective Computational Techniques

Worldwide, anemia is the most common blood disease. The World Health Organization (WHO) defines anemia as the lack of red blood cells, which prevents the body from carrying enough oxygen to satisfy its requirements. Anemia is characterized by decreased erythrocyte mass, blood hemoglobin, and hemo...

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Main Authors: Swapnil M, Parikh, Dukhbhanjan, Singh, Hemal, Thakker, Murugan, R
Format: Article
Language:English
English
Published: INTI International University 2024
Subjects:
QA75 Electronic computers. Computer science
QA76 Computer software
RB Pathology
Online Access:http://eprints.intimal.edu.my/2021/1/jods2024_39.pdf
http://eprints.intimal.edu.my/2021/2/561
http://eprints.intimal.edu.my/2021/
http://ipublishing.intimal.edu.my/jods.html
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spelling my-inti-eprints.20212024-11-08T02:25:42Z http://eprints.intimal.edu.my/2021/ Optimizing Anemia Detection Using Effective Computational Techniques Swapnil M, Parikh Dukhbhanjan, Singh Hemal, Thakker Murugan, R QA75 Electronic computers. Computer science QA76 Computer software RB Pathology Worldwide, anemia is the most common blood disease. The World Health Organization (WHO) defines anemia as the lack of red blood cells, which prevents the body from carrying enough oxygen to satisfy its requirements. Anemia is characterized by decreased erythrocyte mass, blood hemoglobin, and hemocrit levels. Early detection and accurate diagnosis are essential for effective management and therapy. The study's goal is to develop an algorithm for optimizing anemia detection utilizing an effective computational technique. The study proposed a brand-new Dynamic Gannet-tuned Light Gradient Boosting Machine (DG-LGBM) model for the detection of anemia in typical clinical practice settings. In this study, anemia data is collected from a publicly available dataset from Kaggle. The data was preprocessed using data cleaning and normalization for the obtained data. The study aims to improve the predicted accuracy and efficiency of anemia diagnosis by utilizing clinical and biochemical markers. The results demonstrate that, in comparison to traditional methods, the DG-LGBM model performed better in terms of anemia detection rates, highlighting the potential of computational tools to completely transform anemia screening practices. In a comparative analysis, the proposed model is validated using precision (92%), recall (91.71%) f1-score (93.07%), and accuracy (91.06%) values. In addition to advancing the area of medical diagnostics, this study highlights the significance of technology in enhancing healthcare outcomes for impacted communities. INTI International University 2024-11 Article PeerReviewed text en cc_by_4 http://eprints.intimal.edu.my/2021/1/jods2024_39.pdf text en cc_by_4 http://eprints.intimal.edu.my/2021/2/561 Swapnil M, Parikh and Dukhbhanjan, Singh and Hemal, Thakker and Murugan, R (2024) Optimizing Anemia Detection Using Effective Computational Techniques. Journal of Data Science, 2024 (39). pp. 1-12. ISSN 2805-5160 http://ipublishing.intimal.edu.my/jods.html
institution INTI International University
building INTI Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider INTI International University
content_source INTI Institutional Repository
url_provider http://eprints.intimal.edu.my
language English
English
topic QA75 Electronic computers. Computer science
QA76 Computer software
RB Pathology
spellingShingle QA75 Electronic computers. Computer science
QA76 Computer software
RB Pathology
Swapnil M, Parikh
Dukhbhanjan, Singh
Hemal, Thakker
Murugan, R
Optimizing Anemia Detection Using Effective Computational Techniques
description Worldwide, anemia is the most common blood disease. The World Health Organization (WHO) defines anemia as the lack of red blood cells, which prevents the body from carrying enough oxygen to satisfy its requirements. Anemia is characterized by decreased erythrocyte mass, blood hemoglobin, and hemocrit levels. Early detection and accurate diagnosis are essential for effective management and therapy. The study's goal is to develop an algorithm for optimizing anemia detection utilizing an effective computational technique. The study proposed a brand-new Dynamic Gannet-tuned Light Gradient Boosting Machine (DG-LGBM) model for the detection of anemia in typical clinical practice settings. In this study, anemia data is collected from a publicly available dataset from Kaggle. The data was preprocessed using data cleaning and normalization for the obtained data. The study aims to improve the predicted accuracy and efficiency of anemia diagnosis by utilizing clinical and biochemical markers. The results demonstrate that, in comparison to traditional methods, the DG-LGBM model performed better in terms of anemia detection rates, highlighting the potential of computational tools to completely transform anemia screening practices. In a comparative analysis, the proposed model is validated using precision (92%), recall (91.71%) f1-score (93.07%), and accuracy (91.06%) values. In addition to advancing the area of medical diagnostics, this study highlights the significance of technology in enhancing healthcare outcomes for impacted communities.
format Article
author Swapnil M, Parikh
Dukhbhanjan, Singh
Hemal, Thakker
Murugan, R
author_facet Swapnil M, Parikh
Dukhbhanjan, Singh
Hemal, Thakker
Murugan, R
author_sort Swapnil M, Parikh
title Optimizing Anemia Detection Using Effective Computational Techniques
title_short Optimizing Anemia Detection Using Effective Computational Techniques
title_full Optimizing Anemia Detection Using Effective Computational Techniques
title_fullStr Optimizing Anemia Detection Using Effective Computational Techniques
title_full_unstemmed Optimizing Anemia Detection Using Effective Computational Techniques
title_sort optimizing anemia detection using effective computational techniques
publisher INTI International University
publishDate 2024
url http://eprints.intimal.edu.my/2021/1/jods2024_39.pdf
http://eprints.intimal.edu.my/2021/2/561
http://eprints.intimal.edu.my/2021/
http://ipublishing.intimal.edu.my/jods.html
_version_ 1817849521434001408
score 13.223943

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