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准确预测疾病的新方法:适用于心脏和肝脏疾病

Satyanarayana Poojari¹, B Ismail²

¹Department of Applied Statistics and Data Science, Prasanna School of Public Health, Manipal Academy of Higher Education, Manipal, Karnataka, India.

Journal of education and health promotion

|February 2, 2026

概括

一个新的混合机器学习模型改善了早期发现心脏和肝脏疾病. 这种模型结合了决策树和后勤回归以获得更高的准确性,在平衡和不平衡数据集上表现优于现有的方法.

科学领域:

医疗信息学医疗信息学
计算生物学计算生物学
医疗保健中的机器学习

背景情况:

心脏和肝脏疾病是全球主要的死亡原因.
早期检测对于预防并发症和改善患者结果至关重要.
现有的机器学习模型,如物流回归 (LR) 和决策树 (DT),具有局限性,包括过度拟合和高错误分类率.

研究的目的:

开发一种混合分类模型,结合决策树和物流回归的优势.
为解决预测心脏和肝脏疾病的个别LR和DT模型的局限性.
提高健康科学中预测模型的准确性和效率.

主要方法:

开发了一个混合分类模型,集成决策树和后勤回归.
蒙特卡洛模拟和经验研究进行了性能评估.
拟议的模型与物流回归,决策树,支持向量机,K-最近邻居和随机森林进行了比较,使用心脏和肝脏疾病数据集.

主要成果:

与其他分类模型相比,混合模型在各种样本大小中表现出优异的预测性能.
经验数据显示,预测准确度很高:心脏病的91%,肝病的95%.
该模型有效地处理平衡和不平衡的数据集,而不需要特定的平衡技术.

结论:

开发的混合模型为心脏和肝脏疾病提供了更高的预测准确性,无论数据大小或平衡如何.
这种提高效率有助于更早地检测疾病,并改善临床决策.
该模型通过提供更强大的预测工具,为卫生系统的进步做出了贡献.

关键词:

肝脏疾病肝脏疾病蒙特卡洛方法蒙特卡洛方法后勤模型后勤模型机器学习是机器学习.随机的森林随机的森林支持矢量机器的支持矢量机器.

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