Title: Application of machine learning techniques in chronic disease literature: from citation mapping to research front

Authors: Md. Rakibul Hoque; Jinnatul Raihan Mumu; Peter Wanke; Md. Abul Kalam Azad

Addresses: Department of Management Information Systems, Faculty of Business Studies, University of Dhaka, Bangladesh ' Department of Business and Technology Management, Islamic University of Technology, Gazipur 1704, Bangladesh ' Business Analytics and Economics Research Unit – COPPEAD Graduate Business School, Federal University of Rio de Janeiro, Brazil ' Department of Business and Technology Management, Islamic University of Technology, Board Bazar, Gazipur, 1704, Bangladesh

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.

Keywords: machine learning; chronic disease; diabetes; deep learning; bibliometric analysis; systematic review.

DOI: 10.1504/IJISE.2022.126041

International Journal of Industrial and Systems Engineering, 2022 Vol.42 No.2, pp.193 - 210

Received: 12 Oct 2020
Accepted: 13 Jan 2021
Published online: 10 Oct 2022 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article