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

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

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Abstract

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.

Item Type:	Article
Uncontrolled Keywords:	Anemia, Classification, Random Forest, Logistic Regression, Support Vector Machine
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software T Technology > T Technology (General)
Depositing User:	Unnamed user with email masilah.mansor@newinti.edu.my
Date Deposited:	26 Nov 2024 06:14
Last Modified:	26 Nov 2024 06:16
URI:	http://eprints.intimal.edu.my/id/eprint/2048

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