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Machine learning for mHealth apps quality evaluation: An approach based on user feedback analysis.

Mariem Haoues^1,2, Raouia Mokni^3,4, Asma Sellami²

¹Department of Software Engineering, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University Al-Kharj, Al-Kharj, 11942 Saudi Arabia.

Software Quality Journal

|April 16, 2024

Summary

This summary is machine-generated.

This study used machine learning to analyze user feedback on mobile health apps, evaluating quality based on the ISO/IEC 25010 model. Results show high accuracy in classifying app quality characteristics and sentiment, aiding developers in improving mHealth app quality.

Keywords:

ISO/IEC 25010 quality model Machine learning Natural language processing Quality characteristics Sentiment analysis User feedback mHealth apps

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Area of Science:

* Digital Health and mHealth Applications
* Software Quality Engineering
* Applied Machine Learning

Background:

* Mobile health (mHealth) applications are crucial for user health management and accessing healthcare services.
* Analyzing user feedback is essential for enhancing the quality and user experience of mHealth apps.
* The ISO/IEC 25010 quality model provides a framework for evaluating software quality characteristics.

Purpose of the Study:

* To evaluate mHealth app quality using supervised machine learning algorithms based on user feedback.
* To classify user reviews according to the eight quality characteristics of the ISO/IEC 25010 standard.
* To determine the sentiment (Negative, Positive, Neutral) of user feedback for mHealth apps.

Main Methods:

* Collection of 1682 user reviews from 86 mHealth apps on the Google Play Store.
* Application of machine learning and natural language processing techniques for review classification.
* Utilization of the Stochastic Gradient Descent (SGD) and Support Vector Machine (SVM) classifiers.

Main Results:

* SGD classifier achieved 82.00% accuracy in classifying reviews by ISO/IEC 25010 quality characteristics.
* SVM classifier achieved 90.50% accuracy in classifying user reviews by sentiment (Negative, Positive, Neutral).
* SGD achieved high accuracy (up to 98.00%) in sentiment analysis for Usability, Security, and Compatibility.

Conclusions:

* Machine learning effectively analyzes mHealth app user feedback to assess quality against the ISO/IEC 25010 model.
* The study provides actionable insights for developers to improve specific quality attributes of mHealth applications.
* Sentiment analysis of user reviews can pinpoint areas for enhancement in mHealth app usability, security, and compatibility.