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Machine Learning Models for Classification of Anemia from CBC Results: Random Forest, SVM, and Logistic Regression

In an effort to increase diagnostic efficiency and accuracy, this work investigates the application of machine learning models Random Forest, SVM, and Logistic Regression for the categorization of anemia. Hematocrit and hemoglobin levels were included in the dataset, which was divided into traini...

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Main Authors:	Muhammad Rafli, Aditya, Teguh, Sutanto, Haldi, Budiman, M.Rezqy, Noor Ridha, Usman, Syapotro, Noor, Azijah
Format:	Article
Language:	English English
Published:	INTI International University 2024
Subjects:	QA75 Electronic computers. Computer science QA76 Computer software T Technology (General)
Online Access:	http://eprints.intimal.edu.my/2048/1/jods2024_49.pdf http://eprints.intimal.edu.my/2048/2/589 http://eprints.intimal.edu.my/2048/ http://ipublishing.intimal.edu.my/jods.html
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id	my-inti-eprints.2048
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spelling	my-inti-eprints.20482024-11-26T06:16:36Z http://eprints.intimal.edu.my/2048/ Machine Learning Models for Classification of Anemia from CBC Results: Random Forest, SVM, and Logistic Regression Muhammad Rafli, Aditya Teguh, Sutanto Haldi, Budiman M.Rezqy, Noor Ridha Usman, Syapotro Noor, Azijah QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) In an effort to increase diagnostic efficiency and accuracy, this work investigates the application of machine learning models Random Forest, SVM, and Logistic Regression for the categorization of anemia. Hematocrit and hemoglobin levels were included in the dataset, which was divided into training and testing sets. Using CatBoost, Random Forest outperformed SVM (82.1%) and Logistic Regression (75.1%) with the greatest accuracy (99.2%). SVM and Logistic Regression work well with simpler data, while Random Forest performs best with intricate medical datasets, which makes it perfect for applications involving the detection of anemia. INTI International University 2024-11 Article PeerReviewed text en cc_by_4 http://eprints.intimal.edu.my/2048/1/jods2024_49.pdf text en cc_by_4 http://eprints.intimal.edu.my/2048/2/589 Muhammad Rafli, Aditya and Teguh, Sutanto and Haldi, Budiman and M.Rezqy, Noor Ridha and Usman, Syapotro and Noor, Azijah (2024) Machine Learning Models for Classification of Anemia from CBC Results: Random Forest, SVM, and Logistic Regression. Journal of Data Science, 2024 (49). pp. 1-5. 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 T Technology (General)
spellingShingle	QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) Muhammad Rafli, Aditya Teguh, Sutanto Haldi, Budiman M.Rezqy, Noor Ridha Usman, Syapotro Noor, Azijah Machine Learning Models for Classification of Anemia from CBC Results: Random Forest, SVM, and Logistic Regression
description	In an effort to increase diagnostic efficiency and accuracy, this work investigates the application of machine learning models Random Forest, SVM, and Logistic Regression for the categorization of anemia. Hematocrit and hemoglobin levels were included in the dataset, which was divided into training and testing sets. Using CatBoost, Random Forest outperformed SVM (82.1%) and Logistic Regression (75.1%) with the greatest accuracy (99.2%). SVM and Logistic Regression work well with simpler data, while Random Forest performs best with intricate medical datasets, which makes it perfect for applications involving the detection of anemia.
format	Article
author	Muhammad Rafli, Aditya Teguh, Sutanto Haldi, Budiman M.Rezqy, Noor Ridha Usman, Syapotro Noor, Azijah
author_facet	Muhammad Rafli, Aditya Teguh, Sutanto Haldi, Budiman M.Rezqy, Noor Ridha Usman, Syapotro Noor, Azijah
author_sort	Muhammad Rafli, Aditya
title	Machine Learning Models for Classification of Anemia from CBC Results: Random Forest, SVM, and Logistic Regression
title_short	Machine Learning Models for Classification of Anemia from CBC Results: Random Forest, SVM, and Logistic Regression
title_full	Machine Learning Models for Classification of Anemia from CBC Results: Random Forest, SVM, and Logistic Regression
title_fullStr	Machine Learning Models for Classification of Anemia from CBC Results: Random Forest, SVM, and Logistic Regression
title_full_unstemmed	Machine Learning Models for Classification of Anemia from CBC Results: Random Forest, SVM, and Logistic Regression
title_sort	machine learning models for classification of anemia from cbc results: random forest, svm, and logistic regression
publisher	INTI International University
publishDate	2024
url	http://eprints.intimal.edu.my/2048/1/jods2024_49.pdf http://eprints.intimal.edu.my/2048/2/589 http://eprints.intimal.edu.my/2048/ http://ipublishing.intimal.edu.my/jods.html
_version_	1817849525596848128
score	13.223943

Machine Learning Models for Classification of Anemia from CBC Results: Random Forest, SVM, and Logistic Regression

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