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: | , , , , , |
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| Format: | Article |
| Language: | English English |
| Published: |
INTI International University
2024
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| Subjects: | |
| 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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| Summary: | 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. |
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