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

References

  1. Ministry of Health, Labour and Welfare. "About the Population of our Country". Ministry of Health, Labour and Welfare (2022).
  2. Mobile Society Research Institute. "Over 50% Smartphone Ratio among People in their 70s". Mobile Society Research Institute. 12 June (2019).
  3. "Number of mHealth Apps Available in the Google Play Store from 1st Quarter 2015 to 2nd Quarter 2022". Statista. July (2022).
  4. Jabour Abdulrahman M., et al. "The adoption of mobile health applications among University students in health colleges”. Journal of Multidisciplinary Healthcare 14 (2021): 1267.
  5. World Economic Forum. "Global Gender Gap Report 2022". World Economic Forum. 13. July (2022).
  6. Lee P., et al. "Mobile Health App use among Older Adults". Institute for healthcare policy and innovation National poll on healthy aging University of Michigan. February (2022).
  7. Antezana Gaston., et al. "Do young men and women differ in well-being apps usage? Findings from a randomised trial”. Health Informatics Journal1 (2022): 14604582211064825.
  8. Marketing Applications, Inc. "Surveroid". Surveroid (2022).
  9. Macqueen WM. "Some observations about aims and methods in the teaching of building construction”. The Vocational Aspect of Secondary and Further Education2 (1949): 156-168.
  10. Scikit-learn. "Sklearn.Cluster.KMeans". Scikit-learn Machine learning in Python (2022).
  11. Hartigan John A and Manchek A Wong. "Algorithm AS 136: A k-means clustering algorithm”. Journal of the Royal Statistical Society. Series c (applied statistics) 1 (1979): 100-108.
  12. Nazeer KA Abdul and M P Sebastian. "Improving the Accuracy and Efficiency of the k-means Clustering Algorithm”. Proceedings of the world congress on engineering. London, UK: Association of Engineers London 1 (2009).
  13. Breiman Leo. "Random forests”. Machine Learning1 (2001): 5-32.
  14. Sheykhmousa Mohammadreza., et al. "Support vector machine versus random forest for remote sensing image classification: A meta-analysis and systematic review”. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13 (2020): 6308-6325.
  15. Kirasich Kaitlin., et al. "Random forest vs logistic regression: binary classification for heterogeneous datasets”. SMU Data Science Review3 (2018): 9.
  16. Chawla Nitesh V., et al. "SMOTE: synthetic minority over-sampling technique”. Journal of Artificial Intelligence Research 16 (2002): 321-357.
  17. Viloria Amelec., et al. "Unbalanced data processing using oversampling: Machine learning”. Procedia Computer Science 175 (2020): 108-113.
  18. "Projected Global Digital Health Market Size from 2019 to 2025*". Statista. April (2019).
  19. Van der Maaten Laurens and Geoffrey Hinton. "Visualizing data using t-SNE”. Journal of Machine Learning Research11 (2008).
  20. Devassy Binu Melit and Sony George. "Dimensionality reduction and visualisation of hyperspectral ink data using t-SNE”. Forensic Science International 311 (2020): 110194.
  21. Gimpel, Henner, et al. "Understanding the evaluation of mHealth app features based on a cross-country Kano analysis”. Electronic Markets4 (2021): 765-794.
  22. Kayyali Reem, et al. "Awareness and use of mHealth apps: a study from England”. Pharmacy2 (2017): 33.
  23. Aljaber Tareq and Neil Gordon. "A Hybrid Evaluation Approach and Guidance for mHealth Education Applications”. International Conference on Applied Human Factors and Ergonomics. Springer, Cham, (2017).
  24. Aljaber Tareq., et al. "An evaluation framework for mobile health education software”. 2015 Science and Information Conference (SAI). IEEE, (2015).

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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