Application of machine learning techniques in chronic disease literature: from citation mapping to research front. (4th October 2022)

• Record Type: Journal Article
• Authors: Hoque, Md. Rakibul; Mumu, Jinnatul Raihan; Wanke, Peter; Azad, Md. Abul Kalam
• Is Part Of: International journal of industrial and systems engineering. Volume 42:Number 2(2022)
• Page Start: 193
• Page End: 210
• Publication Date: 2022-10-04
• Subjects: machine learning -- chronic disease -- diabetes -- deep learning -- bibliometric analysis -- systematic review; Systems engineering -- Periodicals; Industrial engineering -- Periodicals; 620.001171
• Languages: English
• ISSNs: 1748-5037

Abstract

This study aims to conduct a hybrid review on applying machine learning techniques in chronic disease literature using both bibliometric and systematic review techniques. The dataset consists of 206 Scopus indexed journal articles from 2004 to 2020. The bibliometric results identify the most contributing authors, journal sources, author network, bibliometric coupling of documents, and the co-citation network. The systematic review reveals the most promising research areas, which include machine learning algorithms integrated with other techniques such as deep learning, artificial neural network, and data mining to predict chronic diseases in gastroenterology, cardiology, and neurology. Although machine learning techniques are rising in popularity in chronic disease literature, there is more room for improvement such as the challenges involved in using machine learning to predict chronic diseases, feasibility studies, and the necessity of rehabilitation and readmission in hospitals to predict a chronic attack.