机器学习方法用于优化医疗保健网站的可用性
Amandeep Kaur1, Jaswinder Singh2, Satinder Kaur3
1Dept. of Computer Science, Guru Nanak Dev University, Amritsar, Punjab, India. Amandeeprsh.cs@gndu.ac.in.
Scientific reports
|April 25, 2025
概括
机器学习模型,如随机森林和回归有效地评估医院网站的可用性. 这项研究强调了用户友好的医疗保健网站对于患者获取医疗信息的重要性.
科学领域:
- 医疗信息学 医疗信息学
- 计算机科学 计算机科学
背景情况:
- 医院网站对于患者获得医疗服务和信息至关重要.
- 评估网站的可用性对于有效的患者参与医疗保健至关重要.
- 在使用机器学习进行医疗保健网站可用性评估的经验研究中存在差距.
研究的目的:
- 用机器学习模型评估医院网站的用户友好性.
- 确定最有效的机器学习算法来评估医疗保健网站的可用性.
- 为优化数字医疗保健平台提供见解.
主要方法:
- 利用决策树,随机森林,回归和支向量的回归模型.
- 开发了一个定制的自动化工具来评估100个医院网站的可用性.
- 使用R平方,平均平方误差 (MSE),平均绝对误差 (MAE) 和解释变异得分 (EVS) 等指标进行评估.
主要成果:
- 随机森林回归实现了98%的准确性,山脊回归实现了87%的准确性.
- 关键指标证实了机器学习模型的预测准确性.
- 交叉验证证明了Ridge回归的稳定性,最小的过拟合.
结论:
- 机器学习,特别是随机森林和回归,对于评估医院网站的可用性是有效的.
- 整体可用性是预测网站性能的一个关键因素.
- 未来的研究应该扩展数据集,纳入用户交互数据,并探索深度学习以进行增强的评估.
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