基于Stacking集成学习的心脏病诊断方法

Heart Disease Diagnosis Method Based on Stacking Ensemble Learning

心脏病是威胁国民健康的主要疾病。将机器学习技术应用于心脏病诊断任务,是临床医疗诊断领域的重大突破。以UCI的心脏病数据集为实验数据,提出一种基于Stacking算法的集成学习模型,对不同的基分类器组合进行集成训练,寻找最优的组合策略。结果表明,基分类器差异性大的组合模型具有更好的分类性能,该方法对于提高分类器性能具有可行性和有效性,在心脏病数据集上的分类准确率达到89.78%,能够较好地解决心脏病的临床诊断问题。

Heart disease is a major disease that threatens the national health. Applying machine learning technology to heart disease diagnosis task is a major breakthrough in the field of clinical medical diagnosis. Taking the heart disease dataset of UCI as experimental data, this paper proposes an ensemble learning model based on Stacking algorithm, carries on ensemble training on different combinations of base classifiers to find the optimal strategy. The results show that the combined model with large differences in base classifiers has better classification performance. The proposed method is feasible and effective for improving classifier capability, and the classification accuracy rate of heart disease dataset reaches 89.78%, which can better solve the clinical diagnosis problems of heart disease.