基于血液常规和生物化学检测数据,构建用于心血管全疾病的机器学习诊断模型
Zhicheng Wang1,2,3, Ying Gu1, Lindan Huang1,2
1Institute for Clinical Medical Research, School of Medicine, The First Affiliated Hospital of Xiamen University, Xiamen University, Xiamen, 361003, Fujian, China.
Cardiovascular diabetology
|September 28, 2024
概括
机器学习模型使用血液测试数据准确诊断心血管疾病. 和白蛋白等关键指标有助于识别这些疾病,为具有成本效益的诊断和预防铺平了道路.
科学领域:
- 生物医学信息学 生物医学信息学
- 计算生物学 计算生物学
- 心脏病学 心脏病学
背景情况:
- 心血管疾病是全球主要的死亡原因.
- 传统的诊断方法往往是昂贵和耗时的.
- 可访问的血液数据为诊断提供了潜在的替代方案.
研究的目的:
- 开发机器学习模型用于使用血液常规和生物化学数据进行心血管疾病诊断.
- 确定与心血管疾病相关的独特血液学和代谢特征.
主要方法:
- 利用了25794名健康个体和32822名心血管疾病患者的数据集.
- 使用后勤回归,随机森林,支持矢量机,XGBoost和深度神经网络构建模型.
- 采用SHAP算法进行模型解释.
主要成果:
- 在心血管疾病预测方面,XGBoost模型实现了高性能 (AUC:0.9921).
- 生物化学标记物,如,总蛋白质,白蛋白和间接白血素是关键预测因素.
- 红细胞计数和葡萄糖等特定特征区分了各种心血管疾病.
结论:
- 机器学习模型有效地使用随时可用的血液数据诊断心血管疾病.
- 确定了用于诊断和区分的关键血液学和代谢指标.
- 这种具有成本效益的方法可以帮助早期诊断和预防工作.
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