Machine Learning in Smart Health Research - A Bibliometric Analysis

Aug 2, 2026

Uploaded by Jiven

Machine learning technologies have reshaped smart health by enabling more efficient diagnosis and decision-making workflows. This bibliometric study examines 192 Scopus records retrieved with a structured search strategy to identify leading publication nations, key research subject areas, major funding sponsors, and frequent research keywords. Findings indicate the earliest relevant work emerged in 2011, while overall output has since surged, with India leading current activity. IEEE Access contributes the highest publication volume, supporting researchers, policy makers, and health professionals in tracking field development.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages10

Reading Time25 min

AccessFree to download

Abstract

Keywords

Introduction

Background and motivation

Data, machine learning, and smart health integration

Prior research and research gap

Aim and bibliometric approach

International Journal of Information Science and Management Vol. 21, No. 1, 2023, 117-126 DOI: 10.22034/ijism.2022.1977616.0 / [https://dorl.net/dor/20.1001.1.20088302.2023.21.1.6.6](https://dorl.net/dor/20.1001.1.20088302.2023.21.1.6.6) Original Research Machine Learning in Smart Health Research: A Bibliometric Analysis Oladosu Oyebisi Oladimeji Assistant Lecturer, Department of Mathematical and Computing Sciences, KolaDaisi University, Ibadan, Nigeria Corresponding Author: [oladimejioladosu@gmail.com](oladimejioladosu@gmail.com) ORCID iD: [https://orcid.org/0000-0001-8835-6156](https://orcid.org/0000-0001-8835-6156) Received: 29 November 2021 Accepted: 25 June 2022 Abstract The advent of new technologies such as Machine Learning has highly influenced the health sector's activities; with this, there is an ease in diagnosis and decision-making processes in the sector. Hence, this study aims to analyze the application of Machine Learning in Smart Health research. This study uses 192 records from the Scopus database based on a well-crafted search term to identify nations with the highest publication output, the principal research subject areas, the top funding sponsors, and research keywords in this subject matter. The result shows that the first document on machine learning in smart health was published in 2011. The research output on this subject has dramatically increased, with India now being the top nation where research in this area is conducted. It was also discovered that the journal IEEE Access has the highest number of publications in this area. This analysis will help researchers, policy developers, and professionals in the health sector to better understand the development of Machine Learning in Smart Health research. Machine Learning in Smart Health portends Growth in the future. Keywords – Smart Health, Machine Learning, Bibliometric Analysis, s-Health, Telehealth, Health Informatics. Introduction The innovative development in Information Technology has been driven by the increase in hardware capabilities and the Growth in software efficiency. The smart health field is not an exemption from this development. Therefore, smart health has been gaining more attention from researchers (Grewal, Kayr & Park, 2019) . Smart health is a new field that normally deals with a set of rules that incorporate prevention, diagnosis, detection, treatment procedures, and management (Kang, Park, Cho & Lee, 2018) . The emergence of smart health has given us the platform to exchange information, which was not possible initially due to distance and other constraints. Currently, health workers can gain access to medical information and give medical advice anytime. Over the years, the health sector has accumulated big data (Joloudari, Saadatfar, Dehzangi,& Shamshirband, 2017; Oladimeji, Oladimeji, & Oladimeji, 2021) . Thus, machine learning is a branch of artificial intelligence that studies and learning patterns from data for recommendation and decision-making purposes and has been integrated into smart health. This is to automate certain processes and make certain decisions. Additionally, the integration of machine learning into smart health has fostered early detection, diagnosis, and treatment of diseases, bringing great benefits to health workers and patients (Reddy, 2018) . Hence, the application of machine learning in smart health can assist healthcare givers in diagnosing symptoms more quickly than health workers (Saifi, Taylor, Allen & Hendel, 2013) . Due to the widespread application of machine learning in smart health, various research has been done along this line. However, no previous study has mapped the current status of machine learning and smart health (Li, Shan, Li, Liu & Pu, 2021) . A little bibliometric analysis related to this subject area has been done, such as machine learning in diabetes prediction (Khedkar & Patel, 2021) and artificial intelligence in managing depressive disorde

FAQs

What is the main purpose of the study on machine learning in smart health research?

To quantify and analyze research outputs on machine learning in smart health using a bibliometric approach.

Which database and how many records are used in the analysis?

The study uses 192 records from the Scopus database based on a well-crafted search term.

What do the results show about publication trends and leading contributors?

The first relevant document appeared in 2011, research output increased dramatically afterward, India is the top nation, and IEEE Access has the highest number of publications.

Aug 2, 2026

Uploaded by Jiven