A comparative study of ensemble machine learning algorithms for brucellosis disease prediction
Detection of brucellosis using artificial intelligence
DOI:
https://doi.org/10.62310/liab.v3i2.119Keywords:
Ensemble machine learning, AdaBoostM1, Vote, Bagging, LogitBoost, BrucellosisAbstract
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
Abnoosian K, Farnoosh R, Behzadi MH. (2023). Prediction of diabetes disease using an ensemble of machine learning multi-classifier models. BMC Bioinformatics 24(1): 337. https://doi.org/10.1186/s12859-023-05465-z
Alqahtani A, Alsubai S, Sha M, Vilcekova L, Javed T. (2022). Cardiovascular disease detection using ensemble learning. Computational Intelligence and Neuroscience e5267498. https://doi.org/10.1155/2022/5267498
Alqaissi EY, Alotaibi FS, Ramzan MS. (2022). Modern machine-learning predictive models for diagnosing infectious diseases. Computational and Mathematical Methods in Medicine e6902321. https://doi.org/10.1155/2022/6902321
Arifuzzaman M, Islam M, Hossain M, Tito MH, Anwar M, Al Fuhaid A. (2021). Application of AI on moisture damage of modified asphalt binders. 4th Smart Cities Symposium (SCS 2021) 307-311. https://doi.org/10.1049/icp.2022.0361
Atallah R, Al-Mousa A. (2019). Heart disease detection using machine learning majority voting ensemble method. 2nd International Conference on New Trends in Computing Sciences (ICTCS), Amman, Jordan. Pp. 1-6. https://doi.org/10.1109/ICTCS.2019.8923053
Bagheri H, Tapak L, Karami M, Hosseinkhani Z, Najari H, Karimi S, Cheraghi Z. (2020a). Forecasting the monthly incidence rate of brucellosis in west of Iran using time series and data mining from 2010 to 2019. PLOS ONE 15(5): e0232910. https://doi.org/10.1371/journal.pone.0232910
Ballesteros-Ricaurte JA, Fabregat R, Carrillo-Ramos A, Parra C, Pulido-Medellín MO. (2022). Systematic literature review of models used in the epidemiological analysis of bovine infectious diseases. Electronics 11(15): 2463. https://doi.org/10.3390/electronics11152463
Breiman L. (1996). Bagging predictors. Machine Learning 24(2): 123-140. https://doi.org/10.1007/BF00058655
Budgaga W, Malensek M, Pallickara S, Harvey N, Breidt FJ, Pallickara S. (2016). Predictive analytics using statistical, learning, and ensemble methods to support real-time exploration of discrete event simulations. Future Generation Computer Systems 56: 360-374. https://doi.org/10.1016/j.future.2015.06.013
Darabi H, Choubin B, Rahmati O, Torabi Haghighi A, Pradhan B, Klove B. (2019). Urban flood risk mapping using the GARP and QUEST models: A comparative study of machine learning techniques. Journal of Hydrology 569: 142-154. https://doi.org/10.1016/j.jhydrol.2018.12.002
Freund Y, Schapire RE. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 55(1): 119-139. https://doi.org/10.1006/jcss.1997.1504
Friedman JH. (2001). Greedy function approximation: A gradient boosting machine. The Annals of Statistics 29(5): 1189-1232.
Hossain M, Haque MS, Arifuzzaman M, Hossain SMZ. (2021a). Artificial neural network based system for distorted image recognition. 3rd Smart Cities Symposium (SCS 2020) 503-508. https://doi.org/10.1049/icp.2021.0852
Kundu R, Das R, Geem ZW, Han GT, Sarkar R. (2021). Pneumonia detection in chest X-ray images using an ensemble of deep learning models. PLOS ONE 16(9): e0256630. https://doi.org/10.1371/journal.pone.0256630
Mahdavi A, Aziz J. (2020). Estimation of semiarid forest canopy cover using optimal field sampling and satellite data with machine learning algorithms. Journal of the Indian Society of Remote Sensing 48(4): 575-583. https://doi.org/10.1007/s12524-020-01102-x
Nikhila. (2021). Chronic Kidney Disease Prediction using Machine Learning Ensemble Algorithm. 2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), Greater Noida, India. Pp. 476–480. https://doi.org/10.1109/ICCCIS51004.2021.9397144
Rahman S, Sarker AS, Melzer F, Sprague LD, Neubauer H. (2014). The prevalence of Brucella abortus DNA in seropositive bovine sera in Bangladesh. African Journal of Microbiology 8(48): 3856-3860. https://doi.org/10.5897/AJMR2014.6031
Rushd S, Hafsa N, Al-Faiad M, Arifuzzaman M. (2021). Modeling the settling velocity of a sphere in newtonian and non-newtonian fluids with machine-learning algorithms. Symmetry 13(1): 71. https://doi.org/10.3390/sym13010071
Santangelo OE, Gentile V, Pizzo S, Giordano D, Cedrone F. (2023). Machine learning and prediction of infectious diseases: A systematic review. Machine Learning and Knowledge Extraction 5(1): 175-198. https://doi.org/10.3390/make5010013
Tapak L, Shirmohammadi-Khorram N, Hamidi O, Maryanaji Z. (2018). Predicting the frequency of human brucellosis using climatic indices by three data mining techniques of radial basis function, multilayer perceptron and nearest neighbour. A comparative study. Iranian Journal of Epidemiology 14: 153-165.
Tito M, Arifuzzaman M, Jannat M, Nasrin A, Asaduzzaman M, Hossain M, Maruf S, Asif AH. (2023). Application of specialized machine learning for the prediction of Brucellosis disease. Open Journal of Clinical and Medical Reports 9(27): 2091.
Vapnik VN. (2000). The nature of statistical learning theory. Springer, New York. https://doi.org/10.1007/978-1-4757-3264-1
Verma AK, Pal S, Kumar S. (2020). Prediction of skin disease using ensemble data mining techniques and feature selection method - A comparative study. Applied Biochemistry and Biotechnology 190(2): 341-359. https://doi.org/10.1007/s12010-019-03093-z
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Accepted 2023-10-02
Published 2023-10-03