预测孟加拉国心血管疾病的机器学习方法:2023年横截面研究的证据
Sorif Hossain1, Mohammad Kamrul Hasan2, Mohammad Omar Faruk3
1Department of Statistics, Noakhali Science and Technology University, Noakhali, 3814, Bangladesh. shossain9@isrt.ac.bd.
BMC cardiovascular disorders
|April 17, 2024
概括
心血管疾病 (CVD) 越来越令人担忧,特别是在孟加拉国. 随机森林模型在预测心血管疾病风险方面表现出卓越的准确性 (98.04%) 和精度 (96.15%),为临床实践提供了一个有希望的工具.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 公共卫生 公共卫生
背景情况:
- 心血管疾病 (CVD) 是全球主要的死亡原因.
- 孟加拉国面临着心血管疾病的负担不断增加,超过了传染病.
- 心力衰竭和中风等心血管疾病对低收入和中等收入国家 (LMICs) 产生重大影响.
研究的目的:
- 确定影响心血管疾病的关键因素.
- 开发和评估用于预测心血管疾病风险的机器学习模型.
- 增强对心血管疾病预后的临床决策.
主要方法:
- 分析了391名心血管疾病患者和260名对照组的数据集.
- 统计测试 (交叉点,奇方) 评估了变量关联.
- 包括后勤回归,天真贝叶斯,决策树,AdaBoost,随机森林和合并方法在内的分类器被用于CVD预测.
- 使用准确度,灵敏度,特异性和AU-ROC来评估性能.
主要成果:
- 随机森林分类器实现了最高的准确性 (98.04%) 和精度 (96.15%).
- 随机森林展示了强大的召回 (100%) 和高F1得分 (97.7%).
- 获得0.989的接受器运营商特征 (AU-ROC) 曲线下的最高面积值是随机森林.
结论:
- 随机森林技术强烈推用于开发心血管疾病预测系统.
- 这种预测模型可以作为临床医生评估患者心血管疾病预后的宝贵工具.
- 实施这种模型有可能对治疗心血管疾病的临床实践产生重大影响.
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