Acta Scientific Computer Sciences

Research Article Volume 4 Issue 12

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

Kuniko Azuma1*, Tareq Al Jaber2 and Neil Gordon2

1MSc Artificial Intelligence and Data Science, University of Hull, United Kingdom
2School of Computer Science, University of Hull, United Kingdom

*Corresponding Author: Kuniko Azuma, MSc Artificial Intelligence and Data Science, University of Hull, United Kingdom.

Received: October 18, 2022; Published: November 15, 2022

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.

Keywords: MHA (Mobile Health Application); m-Health; K-means; Clustering; Random Forest

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Citation

Citation: Kuniko Azuma., et al. “Creating a Classification Module to Analysis the Usage of Mobile Health Apps". Acta Scientific Computer Sciences 4.12 (2022): 34-42.

Copyright

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.




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