标题： 使用机器学习算法预测心脏病并减少调查时间 显示英文标题

标题： Predicting Heart Disease and Reducing Survey Time Using Machine Learning Algorithms

摘要： 目前，许多研究人员和分析师正在致力于各种疾病的医疗诊断增强。 心脏病是全球范围内可能导致死亡的重要疾病之一。 早期检测心脏病显著有助于降低心力衰竭的风险。 因此，疾病控制与预防中心（CDC）每年从超过40万名参与者中进行一次与健康相关的电话调查。 然而，关于数据在预测心脏病方面的可靠性以及所有调查问题是否都密切相关存在一些担忧。 本研究旨在利用多种机器学习技术，如支持向量机和逻辑回归，来研究美国CDC的心脏病调查的准确性。 此外，我们使用各种特征选择方法来确定最相关的问卷子集，这些子集可用于预测心脏状况。 为了得出稳健的结论，我们通过随机抽样数据300次来进行稳定性分析。 实验结果表明，调查数据在预测心脏病方面可以达到80%的有用性，这显著提高了血液检查和测试之前的诊断过程。 此外，在保持相同性能水平的情况下，进行调查所花费的时间可以减少77%。 显示英文摘要

摘要： Currently, many researchers and analysts are working toward medical diagnosis enhancement for various diseases. Heart disease is one of the common diseases that can be considered a significant cause of mortality worldwide. Early detection of heart disease significantly helps in reducing the risk of heart failure. Consequently, the Centers for Disease Control and Prevention (CDC) conducts a health-related telephone survey yearly from over 400,000 participants. However, several concerns arise regarding the reliability of the data in predicting heart disease and whether all of the survey questions are strongly related. This study aims to utilize several machine learning techniques, such as support vector machines and logistic regression, to investigate the accuracy of the CDC's heart disease survey in the United States. Furthermore, we use various feature selection methods to identify the most relevant subset of questions that can be utilized to forecast heart conditions. To reach a robust conclusion, we perform stability analysis by randomly sampling the data 300 times. The experimental results show that the survey data can be useful up to 80% in terms of predicting heart disease, which significantly improves the diagnostic process before bloodwork and tests. In addition, the amount of time spent conducting the survey can be reduced by 77% while maintaining the same level of performance.