Medical School, Akfa University, Tashkent, Uzbekistan.
Department of Computer Science Engineering, Panimalar Engineering College, Chennai, Tamil Nadu, India.
J Healthc Eng. 2022 May 14;2022:6503714. doi: 10.1155/2022/6503714. eCollection 2022.
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.
受体和供体的各种特征都会影响肾脏移植物的长期和短期存活。预测肾脏移植的效果对于优化器官分配至关重要。这将使患者能够选择最佳的可利用的肾脏供体和最佳的免疫抑制药物。已有多项研究试图确定导致移植物排斥的因素，但结果相互矛盾。因此，本文的目的是使用基于非洲水牛的人工神经网络 (AB-ANN) 方法来发现与肾脏移植物相关的预测风险变量。这两种特征选择方法相结合，提供了一种新颖的混合特征选择技术，可以选择最重要的元素来提高预测准确性。特征分析表明，临床特征对移植的存活有不同的影响。收集的数据在训练和测试方法中进行处理。以准确性、精确性、召回率和 F 度量为指标，对预测模型的性能进行了评估，并与其他现有系统（包括朴素贝叶斯、随机森林和 J48 分类器）的结果进行了比较。结果表明，与其他分类器相比，该方法可以更准确地预测肾脏受者下一次就诊时的移植物存活情况。与其他分类器相比，该方法在预测肾脏移植物存活方面更有效。将这些临床工具纳入门诊日常工作流程中，可以帮助医生做出更好和更个性化的决策。
