A Bibliometric Survey on Kidney Disease Prediction and Classification

Abstract

In this paper, a bibliometric survey has been carried out on Kidney Disease Prediction and Analysis. The kidney is one of the most vital organs in the human body. Recent trends in research are based on neural networks and machine learning. More specifically, Neural Networks are used to detect the defective kidney and its abnormal region in images and to classify those images. The Scopus database has been used to analyses the documents published on this topic. There are a total of 1551 documents found on the topic of Kidney Disease Prediction and Classification.  The statistical analysis is carried out source-wise, on methods used to predict and classify kidney diseases using image processing methods, AI, NN, and advanced methodologies, year-wise, area-wise, country-wise, university-wise, and based on funding agency. Network analysis is also carried out based on co-authorship, co-occurrence, Citation Analysis, and Bibliographic coupling. The analysis illustrates that the number of papers published is increasing year-wise from 2002 to 2020, indicating the growing research prospects in the area.. The highest number of publications is in the year 2020, and the number of documents published in this year is 199. VOSviewer 1.6.16 software is used for statistical analysis and network analysis of the database. It provides a very effective way to analyze the co-authorship, co-occurrences, citations and bibliometric couplings etc., the source for all tables and figures is www.scopus.com, and the data was assessed on July 16, 2021.