A Bibliometric Survey on Heart Disease Prediction and Classification
In this paper, Bibliometric survey has been carried out on Heart Disease Prediction and Classification from 2000 to 2021. The heart is at the centre of circulatory system, which is a network of blood vessels that delivers blood to every part of the body. Blood carries oxygen and other important nutrients that all body organs need to stay healthy and to work properly. Recent trends in research are based on Artificial Intelligence and Machine learning. More specifically Neural Networks are used to detect abnormal region in Images and to classify the Images. Good number of papers are published in Scopus indexed journals and conferences on this topic. Scopus database has been used to analyse the documents published on this topic. There were total 1904 documents found on the topic “Heart Disease Prediction and Classification”. The statistical analysis is carried out source wise, methods used to predict heart disease specifically Image processing methods, AI, NN and advanced methodologies, year wise, area wise, Country wise, University wise, author 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 shows that the number of papers published is increasing year wise from 2014 to 2021. This show that still there is a great scope for research on this topic. Highest number of publications is in the year 2020 and the number of documents published in this year is 402. VOSviewer1.6.16 software is used for the statistical analysis and network analysis on 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; the data is assessed on 10thJuly, 2021.