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

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.

Analyzing the Patient Behavior for Improving the Medical Treatment Using Smart Healthcare and IoT-Based Deep Belief Network.

Patient behavioral analysis is a critical component in treating patients with a variety of issues, with head trauma, neurological disease, and mental illness. The analysis of the patient's behavior aids in establishing the disease's core cause. Patient behavioral analysis has a number of contests that are much more problematic in traditional healthcare. With the advancement of smart healthcare, patient behavior may be simply analyzed. A new generation of information technologies, particularly the Internet of Things (IoT), is being utilized to transform the traditional healthcare system in a variety of ways. The Internet of Things (IoT) in healthcare is a crucial role in offering improved medical facilities to people as well as assisting doctors and hospitals. The proposed system comprises of a variety of medical equipment, such as mobile-based apps and sensors, which is useful in collecting and monitoring the medical information and health data of patient and interact to the doctor via network connected devices. This research may provide key information on the impact of smart healthcare and the Internet of Things in patient beavior and treatment. Patient data are exchanged via the Internet, where it is viewed and analyzed using machine learning algorithms. The deep belief neural network evaluates the patient's particulars from health data in order to determine the patient's exact health state. The developed system proved the average error rate of about 0.04 and ensured accuracy about 99% in analyzing the patient behavior.