A comparative study of ensemble machine learning algorithms for brucellosis disease prediction: Detection of brucellosis using artificial intelligence

Mokammel Hossain Tito; Md.  Arifuzzaman; Most Hoor E. Jannat; Md. Siddiqur  Rahman; Sayra Tasnin Sharmy; Alifa Nasrin; M.  Asaduzzaman; Md.  Ashrafuzzaman; Dipok Biswas   Prince; Afzal Haq  Asif

doi:10.62310/liab.v3i2.119

Authors

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

DOI:

https://doi.org/10.62310/liab.v3i2.119

Keywords:

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

Abstract

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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References

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