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

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

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