Deep Learning Algorithms for the Prediction of Renal Failure: A Case Study from Tanzania's Muhimbili National Hospital

dc.authorscopusid58979095900
dc.authorscopusid18633882200
dc.authorscopusid58979137900
dc.authorscopusid58978924400
dc.contributor.authorFihavango,T.
dc.contributor.authorIgira,F.
dc.contributor.authorTalawa,N.
dc.contributor.authorPhillip,S.
dc.date.accessioned2024-06-23T21:38:40Z
dc.date.available2024-06-23T21:38:40Z
dc.date.issued2023
dc.departmentKadir Has Universityen_US
dc.department-tempFihavango T., Kadir Has Univerisity, Department of Molecular Biology and Genetics, Istanbul, Turkey; Igira F., The Institute of Finance Management, Department of Computer Science, Dar es Salaam, Tanzania; Talawa N., College of Business Education, Department of Information Communication Technology and Mathematics, Dodoma, Tanzania; Phillip S., Mbeya University of Science and Technology, Department of Medical Science and Technology, Mbeya, Tanzaniaen_US
dc.descriptionIEEE; The Nelson Mandela, African Institution of Science and Technology, Academia for Society and Industry; The University of Dodomaen_US
dc.description.abstractRenal failure occurs when kidney function fails, and the nephron is the major engine for renal function. Patients fail to detect kidney disease in its early stages, leaving them with just two options: kidney transplantation or renal dialysis, both of which are excessively expensive, with dialysis costing roughly 27,440 USD per year and kidney transplants costing 45,000 USD. As a result, developing the framework for the decision support system will aid doctors in reaching the study's goal. This study proposes that Deep learning be used to predict renal failure before it proceeds to the chronic stage. The data set was given by Tanzania's Muhimbili National Hospital. The framework predictor of renal failure was determined using six machine learning techniques (Logistic Regression, Linear Discriminant Analysis, K-Neighbors Classifier, Decision Tree Classifier, Gaussian Naive Bayes, and Support Vector Machine). The best performance was reported to be a Gaussian Naive Bayes and Decision Tree classifier with 100% accuracy, followed by a Support Vector Machine and Logistic Regression with 98.6% accuracy. Cross-validation, confusion matrix, and receiver operating characteristics were also used to evaluate the framework. The precision measurements, recall measures, and f1-score scores serve as the foundation for the renal failure framework's 100% accuracy performance. We proposed that Gaussian Naive Bayes and Decision Tree classifiers be used to predict renal failure. © 2023 IEEE.en_US
dc.identifier.citation0
dc.identifier.doi10.1109/AAIAC60008.2023.10465270
dc.identifier.isbn979-835033013-7
dc.identifier.scopus2-s2.0-85189937132
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/AAIAC60008.2023.10465270
dc.identifier.urihttps://hdl.handle.net/20.500.12469/5820
dc.identifier.wosqualityN/A
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof1st International Conference on the Advancements of Artificial Intelligence in African Context, AAIAC 2023 -- 1st International Conference on the Advancements of Artificial Intelligence in African Context, AAIAC 2023 -- 15 November 2023 through 16 November 2023 -- Arusha -- 198242en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectConfusion matrixen_US
dc.subjectCross Validationen_US
dc.subjectData clearingen_US
dc.subjectData miningen_US
dc.subjectDeep learningen_US
dc.subjectKDDen_US
dc.subjectMachine learningen_US
dc.subjectRenal failureen_US
dc.subjectROCen_US
dc.titleDeep Learning Algorithms for the Prediction of Renal Failure: A Case Study from Tanzania's Muhimbili National Hospitalen_US
dc.typeConference Objecten_US
dspace.entity.typePublication

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