# Machine Learning-Based Disease Diagnosis:A Bibliometric Analysis

arXiv: https://arxiv.org/abs/2201.02755 · HTML: https://mlsanity.com/paper/2201.02755/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 32 references that are in the map.
- First posted on arXiv 8 Jan 2022.

## Builds on (most cited first, 5 of 5)

- [Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning](https://mlsanity.com/paper/1602.03409/) (2016)
- [A Deep Learning Framework for Unsupervised Affine and Deformable Image Registration](https://mlsanity.com/paper/1809.06130/) (2018)
- [Machine learning based disease diagnosis: A comprehensive review](https://mlsanity.com/paper/2112.15538/) (2021)
- [Machine Learning-Based Heart Disease Diagnosis: A Systematic Literature Review](https://mlsanity.com/paper/2112.06459/) (2021)
- [The Journal Coverage of Web of Science and Scopus: a Comparative Analysis](https://mlsanity.com/paper/1511.08096/) (2015)

## Abstract (arXiv)

Machine Learning (ML) has garnered considerable attention from researchers and practitioners as a new and adaptable tool for disease diagnosis. With the advancement of ML and the proliferation of papers and research in this field, a complete examination of Machine Learning-Based Disease Diagnosis (MLBDD) is required. From a bibliometrics standpoint, this article comprehensively studies MLBDD papers from 2012 to 2021. Consequently, with particular keywords, 1710 papers with associate information have been extracted from the Scopus and Web of Science (WOS) database and integrated into the excel datasheet for further analysis. First, we examine the publication structures based on yearly publications and the most productive countries/regions, institutions, and authors. Second, the co-citation networks of countries/regions, institutions, authors, and articles are visualized using R-studio software. They are further examined in terms of citation structure and the most influential ones. This article gives an overview of MLBDD for researchers interested in the subject and conducts a thorough and complete study of MLBDD for those interested in conducting more research in this field.