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机器学习用于mHealth应用程序质量评估:基于分析用户反的方法.

Mariem Haoues1,2, Raouia Mokni3,4, 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
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概括

这项研究使用机器学习来分析用户对移动健康应用程序的反,根据ISO/IEC 25010模型评估质量. 结果显示,在分类应用程序质量特征和情绪方面,其准确度很高,有助于开发人员提高mHealth应用程序质量.

关键词:
在ISO/IEC 25010质量模型中.机器学习 机器学习自然语言处理自然语言处理.质量特征 质量特征情绪分析是一种情绪分析.用户反是用户反.移动健康应用程序

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科学领域:

  • * 数字健康和移动健康应用
  • *软件质量工程 *软件质量工程
  • * 应用机器学习

背景情况:

  • *移动健康 (mHealth) 应用程序对于用户的健康管理和获得医疗保健服务至关重要.
  • *分析用户反对于提高mHealth应用程序的质量和用户体验至关重要.
  • *ISO/IEC 25010质量模型为评估软件质量特征提供了一个框架.

研究的目的:

  • *通过基于用户反的监督机器学习算法来评估mHealth应用程序的质量.
  • *根据ISO/IEC 25010标准的八个质量特征对用户评论进行分类.
  • * 确定用户对mHealth应用程序的反的情绪 (负面,积极,中立).

主要方法:

  • * 收集了来自谷歌Play商店的86个mHealth应用程序的1682个用户评论.
  • * 机器学习和自然语言处理技术的应用用于审查分类.
  • *利用随机梯度下降 (SGD) 和支向量机 (SVM) 分类器.

主要成果:

  • *SGD分类器通过ISO/IEC 25010质量特征对审查进行分类,达到82.00%的准确性.
  • * SVM分类器在根据情绪 (负面,积极,中立) 进行用户评论分类时,获得了90.50%的准确性.
  • *SGD在可用性,安全性和兼容性方面的情绪分析中实现了高准确性 (高达98.00%).

结论:

  • *机器学习有效地分析mHealth应用用户反,以根据ISO/IEC 25010模型评估质量.
  • * 该研究为开发人员提供了可操作的见解,以改善移动健康应用程序的特定质量属性.
  • * 用户评论的情感分析可以确定mHealth应用程序可用性,安全性和兼容性方面的改进领域.