根据分类和非分类属性预测慢性病,使用不同的机器学习算法进行预测
1Department of Computer Applications, VBS Purvanchal University, Jaunpur, India.
概括
早期发现慢性病 (CKD) 是至关重要的. 使用机器学习和多数投票的新模型将CKD分类的准确性提高3%,有助于患者的护理和治疗计划.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學.
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
背景情况:
- 慢性病 (CKD) 由于无症状进展而带来诊断挑战.
- 早期发现功能衰竭对于启动透析或移植至关重要.
- 机器学习模型越来越多地用于医疗保健中的疾病预测和管理.
研究的目的:
- 开发一种针对慢性病 (CKD) 的准确早期检测模型.
- 评估基线分类器在不同数据类型 (分类,非分类和组合) 上的有效性.
- 通过多数投票组合方法提高分类性能.
主要方法:
- 在分别对分类和非分类属性的基础分类器的应用.
- 集成使用多数投票机制的分类器来结合预测.
- 与现有模型进行比较分析,以评估准确度的改进.
主要成果:
- 拟议的模型,利用基线分类器和多数投票,显示准确度增加了3%.
- 整体方法在分类慢性病方面表现得更好.
- 这些发现支持开发的CKD分类模型的提高准确性.
结论:
- 开发的机器学习模型为早期和准确检测CKD提供了一个有希望的方法.
- 多数投票方法有效地提高了分类准确性,有助于临床决策.
- 这项研究有助于提高慢性病的诊断能力,有利于患者的治疗结果.
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