用先进机器学习技术变革心脏病预测
Transforming Heart Disease Prediction with Advanced Machine Learning Techniques
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中文总结 AI 辅助
本研究对比多种ML分类器在UCI、Kaggle心脏病数据集上的性能，发现SVM在UCI数据集、Simple Cart在Kaggle数据集上表现最优，经调参的ML模型可辅助心脏病早期诊断。
中文摘要 AI 辅助
心脏病仍是全球致死的主要原因，需早期准确检测以改善患者预后。本研究聚焦于使用机器学习（ML）技术对心脏病进行预测分析，对比多种分类器的性能，以确定最准确且错误率最低的方法。研究使用了UCI和Kaggle仓库的两个数据集，每个数据集包含14项与心脏健康指标相关的属性。应用的技术包括J48、朴素贝叶斯（Naive Bayes）、逻辑回归（Logistic Regression）、Simple Cart、Bagging、Decision Stump、AdaBoost、人工神经网络（Artificial Neural Networks）和支持向量机（SVM）。采用平均绝对误差（MAE）、相对绝对误差（RAE）、准确率、精确率、召回率和F值等评估指标进行性能对比。结果显示，SVM在UCI数据集上表现最佳，而Simple Cart在Kaggle数据集上表现最优，具备最高准确率和最低错误率。研究结论表明，经适当调参与验证的ML模型可显著辅助心脏病早期诊断，为临床决策提供关键支持；未来工作可涉及混合方法及使用更新的数据集，以进一步提升预测准确率。
英文摘要
Heart disease remains the leading cause of mortality globally, necessitating early and accurate detection to improve patient outcomes. This research focuses on the predictive analysis of heart disease using machine learning (ML) techniques, comparing the performance of multiple classifiers to identify the most accurate and least error-prone method. Two datasets from UCI and Kaggle repositories were utilized, each containing 14 attributes related to heart health indicators. Techniques including J48, Naive Bayes, Logistic Regression, Simple Cart, Bagging, Decision Stump, AdaBoost, Artificial Neural Networks, and Support Vector Machine (SVM) were applied. Evaluation metrics such as Mean Absolute Error (MAE), Relative Absolute Error (RAE), accuracy, precision, recall, and F-measure were used for performance comparison. Results revealed that SVM achieved the highest performance on the UCI dataset, while Simple Cart performed best on the Kaggle dataset, offering the highest accuracy and lowest error rates. The research work concludes that ML models, when properly tuned and validated, can significantly assist in the early diagnosis of heart disease, offering critical support for clinical decision-making. Future work may involve hybrid approaches and the use of more recent datasets to further improve prediction accuracy.
