预测2保护:机器学习网络应用程序,用于早期检测心脏病
1Center for Medical Sciences, Mills E. Godwin High School, Richmond, USA.
Cureus
|November 29, 2023
概括
一个新的心脏病风险评估工具Predict2Protect使用决策树模型进行95%准确的预测. 这个可访问的应用程序旨在改善全球的早期诊断和治疗.
科学领域:
- 心血管健康 心血管健康
- 医疗保健中的机器学习
- 公共卫生技术 公共卫生技术
背景情况:
- 心脏病是全球主要的死亡原因.
- 由于医疗保健的准入和成本障碍,诊断迟到导致结果差.
- 需要为早期心脏病风险识别提供可访问的工具.
研究的目的:
- 开发一个可访问的应用程序,准确预测心脏病风险.
- 为患者提供一个易于使用的界面来评估他们的风险.
- 促进对心脏病的早期发现和干预.
主要方法:
- 一个机器学习模型,Predict2Protect,是使用Python开发的.
- 预先处理了1025名患者的开源数据集,并将其分割为模型培训和测试.
- 评估了四种机器学习模型,并根据其性能选择了一个决策树模型.
主要成果:
- 决策树模型在培训数据上达到了100%的准确性,在测试数据上达到了95%的准确性.
- 该应用程序与Streamlit构建,提供95%准确的心脏病风险评估.
- 该工具估计了未来一年内患心脏病的百分比风险.
结论:
- 预测2保护为心脏病风险评估提供了一个高度准确和易于使用的方法.
- 该应用程序可以赋予个人有关心血管健康的知识.
- 这种工具有可能改善全球对早期心脏病检测和治疗的准入.
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