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Machines: Problem Solving II01:30

Machines: Problem Solving II

Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.

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在使用机器学习的儿童中识别自我照顾问题.

Maya John1, Hadil Shaiba2

  • 1Artificial Intelligence and Data Analytics (AIDA) Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia.

Heliyon
|March 11, 2024
PubMed
概括

机器学习模型现在可以以99%的准确度识别儿童自我照顾问题. 这种方法简化了诊断,解决了医疗专业人员和职业治疗师面临的挑战.

关键词:
分类 分类 分类 分类.不平衡的数据不平衡的数据自我照顾问题 自我照顾问题机器学习是机器学习.

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科学领域:

  • 儿科健康 儿科健康
  • 人工智能的人工智能
  • 医疗保健中的机器学习

背景情况:

  • 对于医疗专业人员来说,识别儿童的自我护理问题是复杂而耗时的.
  • 全球职业治疗师短缺加剧了诊断这些问题的挑战.
  • 机器学习为简化和加快识别过程提供了一个潜在的解决方案.

研究的目的:

  • 使用机器学习模型来识别儿童的自我护理问题.
  • 评估SCADI数据集上的不同分类算法的有效性.
  • 提高儿科自我护理评估的诊断准确性和效率.

主要方法:

  • 利用SCADI数据集进行训练和测试机器学习模型.
  • 通过数据缩小技术解决了高维度问题.
  • 应用SMOTE (合成少数人过量采样技术) 来平衡不平衡的数据集.
  • 比较了天真贝叶斯,J48和随机森林分类算法.

主要成果:

  • 随机森林分类器在SMOTE平衡数据上取得了最高的表现.
  • 在识别自我护理问题时,实现了99%的平衡准确度.
  • 开发的机器学习模型超过了现有的专家系统的性能.

结论:

  • 机器学习,特别是带有SMOTE平衡的随机森林,对于识别儿童自我照顾问题非常有效.
  • 这种方法为传统诊断方法提供了更准确,更有效的替代方案.
  • 该研究表明,人工智能有潜力支持医疗保健专业人员进行儿科评估.