[1]蒋美艳,张辉.机器学习算法对心脏病预测效能的研究[J].中国医学物理学杂志,2024,41(7):905-909.[doi:DOI:10.3969/j.issn.1005-202X.2024.07.018]
　JIANG Meiyan,ZHANG Hui,et al.Efficacy of machine learning algorithms for heart disease prediction[J].Chinese Journal of Medical Physics,2024,41(7):905-909.[doi:DOI:10.3969/j.issn.1005-202X.2024.07.018]
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机器学习算法对心脏病预测效能的研究()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
41卷
期数:
2024年第7期
页码:
905-909
栏目:
医学人工智能
出版日期:
2024-07-25

文章信息/Info

Title:
Efficacy of machine learning algorithms for heart disease prediction
文章编号:
1005-202X（2024）07-0905-05
作者:
蒋美艳1; 2; 张辉1; 2
1.广东医科大学第一临床医学院， 广东 湛江 524023； 2.广东医科大学广东省第二人民医院麻醉科， 广东 广州 510317
Author(s):
JIANG Meiyan1;  2;  ZHANG Hui1;  2
1. The First Clinical Medical College, Guangdong Medical University, Zhanjiang 524023, China 2. Department of Anesthesiology, Guangdong Second Provincial General Hospital, Guangdong Medical University, Guangzhou 510317, China
关键词:
机器学习; 心脏病预测; 医疗大数据
Keywords:
machine learning heart disease prediction medical big data
分类号:
R318；TP391
DOI:
DOI:10.3969/j.issn.1005-202X.2024.07.018
文献标志码:
A
摘要:
目的：探索基于机器学习的方法，包括判定树（DT）、随机森林（RF）、支持向量机（SVM）、K最近邻（KNN）和朴素贝叶斯（NB），构建心脏病预测模型，以实现心脏病的准确预测。方法：使用克利夫兰心脏病数据集作为数据源，通过皮尔逊相关系数选择显著特征，使用DT、RF、SVM、KNN和NB算法构建心脏病预测模型，通过准确度、精确度、召回率、F1分数和受试者工作特征曲线下面积（AUC）值等多项指标评估模型性能。结果：研究纳入303个样本，样本13个临床特征中有11个显著特征，RF预测模型获得最高的准确度（0.869）、召回率（0.906）、F1分数（0.879）和AUC值（0.93），NB预测模型获得最高的精确度（0.900）。结论：基于机器学习的方法能够有效进行心脏病预测，特别是RF预测模型具有显著优势，NB预测模型也表现出令人满意的效果。
Abstract:
Objective To explore the prediction of heart diseases using machine learning-based methods, including decision trees (DT), random forest (RF), support vector machine (SVM), K-nearest neighbors (KNN), and naive Bayes (NB). Methods The Cleveland heart disease dataset was utilized as the data source. Significant features were selected using Pearson correlation coefficients. Heart disease prediction models were constructed using DT, RF, SVM, KNN, and NB algorithms, separately, and the model performance was evaluated with multiple metrics, including accuracy, precision, recall rate, F1 score, and AUC value. Results The study included 303 samples, and among the 13 clinical features, 11 were found to be significant. RF prediction model achieved the highest accuracy (0.869), recall rate (0.906), F1 score (0.879), and AUC value (0.93), while NB prediction model obtained the highest precision (0.900). Conclusion Machine learning-based methods are promising in heart disease prediction, with the RF prediction model demonstrating significant advantages and NB prediction model exhibiting satisfactory performance.

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备注/Memo

备注/Memo:
【收稿日期】2024-01-20 【基金项目】广东省重点领域研发计划（2020B0101130020） 【作者简介】蒋美艳，硕士研究生，研究方向：麻醉、心脏病预测，E-mail: jmygmu@163.com 【通信作者】张辉，教授，研究方向：术后疼痛、机器学习，E-mail: zhanghui@gd2h.org.cn
更新日期/Last Update: 2024-07-13