一个机器学习驱动的单图,用于预测慢性病的3-5阶段
Samit Kumar Ghosh1, Ahsan H Khandoker2
1Healthcare Engineering Innovation Center (HEIC), Department of Biomedical Engineering, Khalifa University, Abu Dhabi, United Arab Emirates. samitnitrkl@gmail.com.
Scientific reports
|December 7, 2023
概括
这项研究开发了一种机器学习 (ML) 编号图,用于早期检测慢性病 (CKD) 3-5阶段. ML模型准确预测CKD风险,为临床预防策略提供了有价值的工具.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
背景情况:
- 慢性病 (CKD) 是全球主要的健康问题,对死亡率有很大影响.
- 准确的预测模型对于早期发现CKD和有效的预防策略至关重要.
- 机器学习 (ML) 在推进医学预测能力方面表现有前途.
研究的目的:
- 引入一种基于机器学习的新型名谱,用于早期识别患有3-5阶段CKD风险的个体.
- 使用临床和实验室数据开发和验证预测模型.
- 与传统预测方法相比,评估ML模型的性能.
主要方法:
- 对一大群被诊断为CKD患者的回顾性分析.
- 应用先进的ML算法,包括特征选择和回归模型 (线性回归,支向量机).
- 开发一种基于LR的名图,利用显著的预测因素 (年龄,性别,病史,实验室结果).
主要成果:
- 在467名参与者中,11.56%的参与者在9年的随访期间发展了CKD 3-5阶段.
- 几个因素表明与CKD发展有独立的显著关联 (p < 0.05).
- 基于LR的模型实现了0.954079的高R得分,超过了SVM和传统模型.
结论:
- 机器学习驱动的诺姆图是用于早期CKD风险识别的卓越和有价值的临床工具.
- 这种方法有助于改善风险管理和预防晚期CKD阶段.
- 需要进一步的研究来完善模型并验证其在不同人群中的表现.
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