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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English English |
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INTI International University
2024
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| Subjects: | |
| Online Access: | http://eprints.intimal.edu.my/2048/ http://eprints.intimal.edu.my/2048/1/jods2024_49.pdf http://eprints.intimal.edu.my/2048/2/589 |
| _version_ | 1848766906336542720 |
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| 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 |
| building | INTI Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-14T11:58:35Z |
| format | Article |
| id | intimal-2048 |
| institution | INTI International University |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-14T11:58:35Z |
| publishDate | 2024 |
| publisher | INTI International University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | intimal-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 |
| 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 |
| title | 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_short | 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 |
| topic | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) |
| url | http://eprints.intimal.edu.my/2048/ http://eprints.intimal.edu.my/2048/ http://eprints.intimal.edu.my/2048/1/jods2024_49.pdf http://eprints.intimal.edu.my/2048/2/589 |