A comparative study of ensemble machine learning algorithms for brucellosis disease prediction

Detection of brucellosis using artificial intelligence


  • Mokammel Hossain Tito Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Bangladesh https://orcid.org/0009-0006-0082-9953
  • Md. Arifuzzaman King Faisal University. Saudi Arabia https://orcid.org/0000-0002-5069-0447
  • Most Hoor E. Jannat Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Bangladesh
  • Md. Siddiqur Rahman Bangladesh Agricultural University, Bangladesh https://orcid.org/0000-0003-4919-9973
  • Sayra Tasnin Sharmy Bangladesh Agricultural University, Bangladesh https://orcid.org/0000-0002-1601-7742
  • Alifa Nasrin Combined Military Hospital, Bangladesh
  • M. Asaduzzaman National Heart Foundation Hospital & Research Institute, Bangladesh
  • Md. Ashrafuzzaman Islamic University Bangladesh
  • Dipok Biswas Prince Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Bangladesh https://orcid.org/0009-0007-6608-746X
  • Afzal Haq Asif King Faisal University. Saudi Arabia https://orcid.org/0000-0002-6941-6050


Ensemble machine learning, AdaBoostM1, Vote, Bagging, LogitBoost, Brucellosis


Brucellosis, caused by Brucella spp., is a global public health concern, particularly in underdeveloped regions. Cattle, predominantly infected with B. abortus, encounter reproductive challenges, reduced productivity, and fertility issues. Effective control measures, including serological tests like iELISA (indirect Enzyme-linked Immunosorbent Assay) are vital. This research harnesses machine learning techniques, encompassing AdaBoostM1, Vote, Bagging, and LogitBoost, to forecast Brucella infection in cattle, utilizing comprehensive data sourced from Qazvin, Iran. Detailed model descriptions are provided, highlighting AdaBoostM1 as the optimal choice, boasting a robust 75% correlation, low RMSE (Route Mean Square Error), MAE (Mean Absolute Error), and a commendable Kappa Statistics score of 0.4965. Ensemble machine learning demonstrates significant potential in Brucellosis prediction, adept at handling intricate datasets, and enhancing predictive accuracy. AdaBoostM1 stands out as the preferred model, offering valuable insights for Brucellosis prediction and contributing to the enhancement of disease control strategies.


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How to Cite

Tito, M. H., Arifuzzaman, M. ., Jannat, M. H. E., Rahman, M. S. ., Sharmy, S. T., Nasrin, A., Asaduzzaman, M. ., Ashrafuzzaman, M. ., Prince, D. B. . ., & Asif, A. H. . (2023). A comparative study of ensemble machine learning algorithms for brucellosis disease prediction: Detection of brucellosis using artificial intelligence. Letters In Animal Biology, 3(2), 23–27. Retrieved from https://liabjournal.com/index.php/liab/article/view/119



Research Articles
Recieved 2023-09-02
Accepted 2023-10-02
Published 2023-10-03