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

Tipo
Artigos

Ano
04/10/2022

Linha de Pesquisa
Administração e Economia de Negócios

Autor(es)
Md. Rakibul Hoque, Jinnatul Raihan Mumu, Peter Wanke, Md. Abul Kalam Azad

Orientador

https://doi.org/10.1504/IJISE.2022.126041


Caso deseje uma cópia integral da tese/dissertação, por favor envie um e-mail para biblioteca@coppead.ufrj.br.

International Journal of Industrial and Systems Engineering, v. 42, n. 2, pp. 193-210. 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

Tipo
Artigos

Ano
04/10/2022

Linha de Pesquisa
Administração e Economia de Negócios

Autor(es)
Md. Rakibul Hoque, Jinnatul Raihan Mumu, Peter Wanke, Md. Abul Kalam Azad

Orientador

https://doi.org/10.1504/IJISE.2022.126041


Caso deseje uma cópia integral da tese/dissertação, por favor envie um e-mail para biblioteca@coppead.ufrj.br.

International Journal of Industrial and Systems Engineering, v. 42, n. 2, pp. 193-210. 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

Rolar para cima