Building a Recommender System to Predict the Shape of Bacteria in Urine Cytobacteriological Examination Using Machine Learning

Authors

DOI:

https://doi.org/10.3991/ijoe.v19i13.36185

Keywords:

Recommender System, Machine Learning, Artificial Intelligence, Urine Cytobacteriological Examination, Random Forest Algorithm, Prediction

Abstract


This study aimed to build a recommender system that predicts the shape of bacteria for biological requests of urine cytobacteriological examination (UCBE) using machine learning techniques, to reduce the time taken to identify the shape of bacteria (Cocci or Bacilli). We used different methods and techniques in the process: Unified Modelling Language (UML) was used for digital design architecture, Rstudio tool with R programming language for system development, and Random Forest (RF) algorithm for the prediction. Experimental results showed that the time needed to identify the shape of bacteria is decreased, and bacilli bacteria are better recognized by the algorithm with an error rate of 3%. In addition to that, the proposed recommender system allows biologists to validate and correct the prediction and improve the accuracy of the classification algorithm used in the future.

Author Biography

Mohammed Ouadoud, National School of Applied Sciences, Abdelmalek Essaâdi University, Morocco

He is a Ph.D. in Computer sciences, at the Laboratory of Informatics, Research Operational and Statistic Applied (LIROSA) at Faculty of Sciences, Abdelmalek Essaâdi University. In 2018, he completed his Ph.D. thesis in computer science at the faculty of science of Tetouan, Morocco. His dissertation research, focus on Modeling and Prototyping a Learning Management System Based on the IMD-LD and the Hybridization between Learning Theories. He has a Master degree in Multimedia Engineering of Instructional Design at the École Normale Supérieure of Martil, Morocco in 2013. His current research focuses on E-learning, Software Engineering, Man Machine Interface, Geomatics, Geotech and Bigdata. Mohammed Ouadoud is a reviewer in several International journals.
Cover iJOE – Vol 19, No 13, 2023

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Published

2023-09-18

How to Cite

Lafraxo, M. A., Hami , H. ., Merrakchi, T. ., Azghar, A. ., Remaida, A. ., Ouadoud, M., Maleb, A., & Soulaymani, A. . (2023). Building a Recommender System to Predict the Shape of Bacteria in Urine Cytobacteriological Examination Using Machine Learning. International Journal of Online and Biomedical Engineering (iJOE), 19(13), pp. 92–107. https://doi.org/10.3991/ijoe.v19i13.36185

Issue

Vol. 19 No. 13 (2023)

Section

Papers

License

Copyright (c) 2023 Mohammed Amine Lafraxo, Hinde Hami , Tarik Merrakchi, Ali Azghar, Ahmed Remaida, Mohammed Ouadoud, Adil Maleb, Abdelmajid Soulaymani

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