Skip to main content

Research Repository

Advanced Search

Creating a Classification Module to Analysis the Usage of Mobile Health Apps

Azuma, Kuniko; Al Jaber, Tareq; Gordon, Neil

Authors

Kuniko Azuma



Abstract

With an ageing society becoming a major issue for many countries, health-related concerns are growing and mobile health applications (MHAs) are rapidly gaining users. The applications available range from those that promote exercise to maintain health, those that help to manage physical condition by recording weight and activity, and those that allow users to consult doctors and pharmacists. On the other hand, there are still many mobile users who do not use MHAs. In this case study from Japan, the range of diverse MHAs were classified into five categories by K-means clustering analysis and the results of a questionnaire on the use of MHAs were analyzed using a scientific approach to find out which types of users mainly use these applications. Based on the results of this analysis, a classifier was created using a Random Forest algorithm to extract MHAs that meet the needs of users based on their attributes and thoughts. With this Random Forest classification model, this paper recommends appropriate models for potential users who are not yet using MHAs.

Citation

Azuma, K., Al Jaber, T., & Gordon, N. (2022). Creating a Classification Module to Analysis the Usage of Mobile Health Apps. Acta Scientific Computer Sciences, 4(12), 34-42

Journal Article Type Article
Acceptance Date Nov 2, 2022
Online Publication Date Nov 15, 2022
Publication Date Dec 1, 2022
Deposit Date Dec 1, 2022
Publicly Available Date Dec 8, 2022
Journal Acta Scientific Computer Sciences
Publisher Acta Scientific
Peer Reviewed Peer Reviewed
Volume 4
Issue 12
Pages 34-42
Keywords MHA (Mobile Health Application); m-Health; K-means; Clustering; Random Forest
Public URL https://hull-repository.worktribe.com/output/4135445
Publisher URL https://actascientific.com/ASCS/ASCS-04-0358.php

Files

Published article (802 Kb)
PDF

Publisher Licence URL
http://creativecommons.org/licenses/by/4.0

Copyright Statement
Copyright: © 2022 Kuniko Azuma., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.






You might also like



Downloadable Citations