[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128505-en":3,"doc-seo-128505-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},128505,687207017582,"Himbo","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Building a Recommender System to Predict the Shape of Bacteria in Urine Cytobacteriological Examination Using Machine Learning - Paper","The study builds a recommender system to predict bacterial shape in urine cytobacteriological examination (UCBE) using machine learning, aiming to reduce the time required to identify bacterial morphology (cocci or bacilli). System development combines UML for digital architecture, RStudio with R for implementation, and a Random Forest (RF) model for prediction. Results show identification time decreases, bacilli are better recognized, and the reported error rate reaches 3%. The system enables biologists to validate and correct predictions and improve future classification accuracy.","JOE International Journal of  \nOnline and Biomedical Engineering  \n[Onli](Online-Journals.org)[ne-Jo](Online-Journals.org)[urnals](Online-Journals.org)[.org](Online-Journals.org)  \niJOE | eISSN: 2626-8493 | Vol. 19 No. 13 (2023) |   \n[https://doi.org/10.3991/ijoe.v19i13.36185](https://doi.org/10.3991/ijoe.v19i13.36185)  \nPAPER  \nBuilding a Recommender System to Predict the Shape of Bacteria in Urine Cytobacteriological Examination Using Machine Learning  \nMohammed Amine Lafraxo1(􀀍), Hinde Hami1, Tarik Merrakchi2, Ali Azghar3,4, Ahmed Remaida1, Mohammed Ouadoud5, Adil Maleb3,4, Abdelmajid Soulaymani1  \n1Faculty of Science, Ibn Tofail University, Kenitra, Morocco  \n2Hassan II University, Casablanca, Morocco  \n3Mohammed First University, Oujda, Morocco  \n4Mohammed VI University  \nHospital, Oujda, Morocco 5Abdelmalek Essaadi University, Tetouan, Morocco [lafraxo.ma@gmail.com](lafraxo.ma@gmail.com)  \nABSTRACT  \nThis 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.  \nKEYWORDS  \nrecommender system, machine learning, artificial intelligence (AI), urine cytobacteriological examination (UCBE), random forest (RF) algorithm, prediction  \n1 INTRODUCTION  \nMachine learning is a scientific field and, more specifically, a subset of artificial intelligence (AI) that enables computers to automatically improve through experience [1] . Specifically, it enables algorithms to autonomously learn to perform a task or make predictions from data and improve their performance over time. The extremely rapid evolution of technological development has had a profound impact on several areas of society, including health. And with the emergence of AI, it improved and became a 21st-century innovation [2] . Over the past decades, several scientific studies and research projects have shown the importance of  \nLafraxo, 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](https://doi.org/10.3991/ijoe.v19i13.36185)[ ](https://doi.org/10.3991/ijoe.v19i13.36185)[Article submitted 2022-10-18. Revision uploaded 2023-02-10. Final acceptance 2023-02-24.](Article submitted 2022-10-18. Revision uploaded 2023-02-10. Final acceptance 2023-02-24.)  \n© 2023 by the authors of this article. Published under CC-BY.  \n92 International Journal of Online and Biomedical Engineering (iJOE) iJOE | Vol. 19 No. 13 (2023)  \nBuilding a Recommender System to Predict the Shape of Bacteria in Urine Cytobacteriological Examination Using Machine Learning  \napplying machine learning techniques through recommender systems in a health context, especially in epidemiology, radiology, biology, and medicine.  \nRecommender systems have been studied in several areas: information retrieval, web technologies, e-commerce, health sciences, educational technologies, and many others. The best-known recommender systems are those used on e-commerce websites. The principle is to use a cus","cbCaiol0FIrBYyVe","https://ap.wps.com/l/cbCaiol0FIrBYyVe","pdf",1409832,1,16,"English","en",105,"# Introduction\n## Machine learning in AI-driven systems\n## Recommender systems and application areas\n## Health context and biology benefits\n## UCBE overview and study objective","[{\"question\":\"What problem does the proposed recommender system address in UCBE?\",\"answer\":\"It predicts bacterial shape (cocci or bacilli) for urine cytobacteriological examination to reduce the time needed for identification.\"},{\"question\":\"Which tools and methods are used to build the system?\",\"answer\":\"The study uses UML for digital design architecture, RStudio with the R programming language for development, and a Random Forest (RF) algorithm for prediction.\"},{\"question\":\"What performance results are reported for bacterial shape recognition?\",\"answer\":\"The approach decreases the time required for identification, with bacilli recognized better by the algorithm and an error rate reported at 3%.\"}]","Building a Recommender System to Predict the Shape of Bacteria in Urine Cytobacteriological Examination Using Machine Learning - 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