使用深度学习,单数值分解和自我组织地图方法诊断糖尿病的综合方法
Mehrbakhsh Nilashi1,2, Rabab Ali Abumalloh3, Sultan Alyami4
1UCSI Graduate Business School, UCSI University, No. 1 Jalan Menara Gading, UCSI Heights, Cheras, Kuala Lumpur 56000, Malaysia.
Diagnostics (Basel, Switzerland)
|May 27, 2023
概括
这项研究引入了一种新的机器学习方法,用于准确预测糖尿病风险,有效处理缺失的数据以改善诊断分类. 开发的方法提高了识别有患糖尿病风险的个体的可靠性.
科学领域:
- 医疗信息学 医疗信息学
- 机器学习 机器学习
- 计算生物学 计算生物学
背景情况:
- 糖尿病是一种日益严重的全球健康危机,需要改进诊断和预测工具.
- 准确的糖尿病患者分类对于数据解释和诊断至关重要.
- 数据集中的缺失值显著阻碍了糖尿病诊断预测模型的准确性.
研究的目的:
- 开发和评估一种用于准确预测糖尿病风险的新型机器学习方法.
- 解决糖尿病数据集中缺少数据的挑战.
- 为了提高糖尿病的分类准确度.
主要方法:
- 使用单数值分解来赋值缺失的值.
- 雇员自组织地图用于数据聚类.
- 应用了STEPDISC来进行功能选择.
- 开发了一组用于预测的深信网络分类器.
主要成果:
- 拟议的方法在预测真实世界数据集上的糖尿病中表现出高准确性.
- 性能与现有的机器学习预测技术进行了比较.
- 综合方法有效地管理了缺失的数据和精选的相关特征.
结论:
- 开发的机器学习方法为糖尿病风险预测提供了强大的解决方案.
- 即使使用不完整的数据集,也可以实现准确的预测.
- 这种方法有可能提高糖尿病管理中的临床决策.
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