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This would allow patients to choose the best accessible kidney donor and the optimal immunosuppressive medication. Several studies have attempted to identify factors that predispose to graft rejection, but the results have been contradictory. As a result, the goal of this paper is to use the African buffalo-based artificial neural network (AB-ANN) approach to uncover predictive risk variables related to kidney graft. These two feature selection approaches combine to provide a novel hybrid feature selection technique that could select the most important elements to improve prediction accuracy. The feature analysis revealed that clinical features have varied effects on transplant survival. The collected data is processed in both training and testing methods. The prediction model's performance, in terms of accuracy, precision, recall, and F-measure, was examined, and the results were compared with those of other existing systems, including naive Bayesian, random forest, and J48 classifier. The results suggest that the proposed approach can forecast graft survival in kidney recipients' next visits in a creative manner and with more accuracy compared with other classifiers. This proposed method is more efficient for predicting kidney graft survival. Incorporating those clinical tools into outpatient clinics\u2019 everyday workflows could help physicians make better and more personalised decisions.<\/jats:p>","DOI":"10.1155\/2022\/6503714","type":"journal-article","created":{"date-parts":[[2022,5,15]],"date-time":"2022-05-15T04:50:08Z","timestamp":1652590208000},"page":"1-9","source":"Crossref","is-referenced-by-count":5,"title":["Predicting the Kidney Graft Survival Using Optimized African Buffalo-Based Artificial Neural Network"],"prefix":"10.1155","volume":"2022","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2867-9655","authenticated-orcid":true,"given":"Riddhi","family":"Chawla","sequence":"first","affiliation":[{"name":"Medical School, Akfa University, Tashkent, Uzbekistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S.","family":"Balaji","sequence":"additional","affiliation":[{"name":"Department of Computer Science Engineering, Panimalar Engineering College, Chennai, Tamil Nadu, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Raed N.","family":"Alabdali","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Science and Arts in Qurayyat, Jouf University, Sakakah, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5923-1466","authenticated-orcid":true,"given":"Ibrahim A.","family":"Naguib","sequence":"additional","affiliation":[{"name":"Department of Pharmaceutical Chemistry, College of Pharmacy, Taif University, P.O. 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