Predicting the Kidney Graft Survival Using Optimized African Buffalo-Based Artificial Neural Network

Author:

Chawla Riddhi1ORCID,Balaji S.2,Alabdali Raed N.3,Naguib Ibrahim A.4ORCID,Hamed Nadir O.5ORCID,Zahran Heba Y.678

Affiliation:

1. Medical School, Akfa University, Tashkent, Uzbekistan

2. Department of Computer Science Engineering, Panimalar Engineering College, Chennai, Tamil Nadu, India

3. Department of Computer Science, College of Science and Arts in Qurayyat, Jouf University, Sakakah, Saudi Arabia

4. Department of Pharmaceutical Chemistry, College of Pharmacy, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia

5. Computer Studies Department, Elgraif Sharg Technological College, Sudan Technological University, Khartoum, Sudan

6. Laboratory of Nano-Smart Materials for Science and Technology (LNSMST), Department of Physics, Faculty of Science, King Khalid University, Abha 61413, Saudi Arabia

7. Research Center for Advanced Materials Science (RCAMS), King Khalid University, Abha 61413, Saudi Arabia

8. Nanoscience Laboratory for Environmental and Biomedical Applications (NLEBA), Metallurgical Laboratory 1, Department of Physics, Faculty of Education, Ain Shams University, Roxy, Cairo 11757, Egypt

Abstract

A variety of receptor and donor characteristics influence long-and short-term kidney graft survival. It is critical to predict the effectiveness of kidney transplantation to optimise organ allocation. 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’ everyday workflows could help physicians make better and more personalised decisions.

Funder

King Khalid University

Publisher

Hindawi Limited

Subject

Health Informatics,Biomedical Engineering,Surgery,Biotechnology

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