使用数据挖掘和机器学习解决方案预测冠心病
Vijai M Moorthy1, Bhupal N Dharamsoth1, Vijayalakshmi Muthukaruppan1
1Vignan's Foundation for Science Technology & Research, Department of Advanced Computer Science and Engineering, 522202 Guntur, Andhra Pradesh, India.
Anais da Academia Brasileira de Ciencias
|June 18, 2025
概括
这项研究使用集体机器学习增强了心血管疾病 (CVD) 的预测. 梯度提升实现了98.3%的准确性,超过了准确评估心血管疾病风险的其他方法.
科学领域:
- 计算生物学是一种计算生物学.
- 医疗信息学医学信息学
- 机器学习在医疗保健中的应用
背景情况:
- 心血管疾病 (CVD) 仍然是全球主要的死亡原因.
- 准确预测心血管疾病风险对于及时干预和改善患者结果至关重要.
- 现有的预测模型可能会从先进的机器学习技术中受益,以提高准确性.
研究的目的:
- 开发和评估一套集体机器学习模型,用于预测心血管疾病.
- 为了比较集成机器学习算法的性能,包括随机森林和梯度提升.
- 使用贝叶斯超参数调优化集团模型,以获得更高的预测能力.
主要方法:
- 开发一种结合线性回归,随机森林和渐变增强算法的新型集体学习模型.
- 贝叶斯超参数调整的应用,以优化模型参数.
- 使用分类准确度和弗雷明汉数据集 (4240个样本) 的真实阳性率评估模型性能.
主要成果:
- 整体模型显示了对心血管疾病结果的高预测性能.
- 梯度提升 (GB) 实现了98.3%的分类准确率和98.3%的真实阳性率.
- 与Framingham数据集上的其他评估算法相比,GB方法显示出优越的预测能力.
结论:
- 整体机器学习,特别是渐变增强,为准确预测心血管疾病提供了强大的策略.
- 开发的模型显示了临床应用的重大潜力,用于识别心血管疾病风险高的患者.
- 在不同的数据集上进行进一步的验证是有必要的,以确认发现的概括性.
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