用机器学习提升心脏病预测准确率，实测最优模型效果显著。
Transforming Heart Disease Prediction with Advanced Machine Learning Techniques
- 对比多种机器学习模型在心病数据上的表现
- SVM和Simple Cart分别在两个数据集上达最高准确率
- 结果可为临床早期诊断提供可靠支持
心脏病仍是全球主要死因，亟需早期精准检测以改善预后。本研究采用机器学习方法对心脏病进行预测分析，比较了包括J48、朴素贝叶斯、逻辑回归、Simple Cart、Bagging、决策桩、AdaBoost、人工神经网络及支持向量机（SVM）在内的多种分类器性能。使用来自UCI和Kaggle的两个数据集，每个含14个与心脏健康相关的属性。通过平均绝对误差（MAE）、相对绝对误差（RAE）、准确率、精确率、召回率及F值等指标评估模型表现。结果显示，在UCI数据集上SVM表现最佳，而在Kaggle数据集上Simple Cart精度最高、误差最低。研究结论表明，经适当调参与验证的机器学习模型能显著辅助心脏病的早期诊断，为临床决策提供关键支持。未来工作可探索混合模型与更近期数据集以进一步提升预测精度。
原文摘要 · Abstract (English)
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.
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