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

Authors

  • Muhammad Rafli Aditya Islamic University of Kalimantan Muhammad Arsyad Al-Banjari, Indonesia
  • Teguh Sutanto Islamic University of Kalimantan Muhammad Arsyad Al-Banjari, Indonesia
  • Haldi Budiman Islamic University of Kalimantan Muhammad Arsyad Al-Banjari, Indonesia
  • M.Rezqy Noor Ridha Islamic University of Kalimantan Muhammad Arsyad Al-Banjari, Indonesia
  • Usman Syapotro Islamic University of Kalimantan Muhammad Arsyad Al-Banjari, Indonesia
  • Noor Azijah Islamic University of Kalimantan Muhammad Arsyad Al-Banjari, Indonesia

Keywords:

Anemia, Classification,, Random Forest, Logistic Regression, Support Vector Machine

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.

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Published

2024-11-26

How to Cite

Aditya, M. R., Sutanto, T., Budiman, H., Ridha, M. N., Syapotro, U., & Azijah, N. (2024). Machine Learning Models for Classification of Anemia from CBC Results: Random Forest, SVM, and Logistic Regression. Journal of Data Science, 2024. Retrieved from https://iuojs.intimal.edu.my/index.php/jods/article/view/589

Issue

Vol. 2024: Journal of Data Science

Section

Articles

License

Copyright (c) 2024 Journal of Data Science

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This work is licensed under a Creative Commons Attribution 4.0 International License.