用机器学习预测心脏病，准确率达84%。
Predicting Coronary Heart Disease Using a Suite of Machine Learning Models
- 采用随机森林结合过采样技术提升预测性能。
- 模型准确率达到84%，优于其他对比方法。
- 适合医疗数据挖掘与早期筛查场景。
冠心病影响全球数百万人，是医疗研究的重点领域。尽管已有多种诊断和预测方法，但存在侵入性、检测滞后或成本高等局限。本文采用多种经典机器学习算法，通过监督学习构建非侵入式、低成本的预测模型，以实现早期诊断。实验表明，使用过采样处理特征变量的随机森林模型表现最佳，准确率达84%。该方法为临床前筛查提供了高效可行的技术路径。
原文摘要 · Abstract (English)
Coronary Heart Disease affects millions of people worldwide and is a well-studied area of healthcare. There are many viable and accurate methods for the diagnosis and prediction of heart disease, but they have limiting points such as invasiveness, late detection, or cost. Supervised learning via machine learning algorithms presents a low-cost (computationally speaking), non-invasive solution that can be a precursor for early diagnosis. In this study, we applied several well-known methods and benchmarked their performance against each other. It was found that Random Forest with oversampling of the predictor variable produced the highest accuracy of 84%.
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