System Development & Application
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 6
1796-1804

Coronary artery disease prediction and feature analysis model based on XGBoost and SHAP

Chen Xiaokun1
Zuo Hangxu1
Liao Bin1
Sun Ruina1,2,3
1. College of Statistics & Data Science, Xinjiang University of Finance & Economics, Urumchi 830012, China
2. Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China
3. School of Networks Security, University of Chinese Academy of Sciences, Beijing 100093, China
DOI: 10.19734/j.issn.1001-3695.2021.11.0639

Abstract

To address the lack of practical application and interpretability of coronary artery disease(CAD) diagnostic models, this paper proposed a novel model based on XGBoost and SHAP for the diagnosis of CAD. Firstly, it put the processed dataset into the XGBoost model for training, and optimized the model to boost performance. Then, compared to six machine learning models such as SVM and naive Bayes and eight mainstream machine learning models, the parameter-optimized XGBoost model obtains 0.994 2, 0.997 0, 0.994 1 and 0.999 8 in accuracy, specificity, F1 and AUC, which are higher than the existing models. Lastly, it used the SHAP framework to improve model interpretability and identified important factors affecting CAD. The proposed model has the potential to be a useful diagnostic tool in hospitals for the diagnosis of CAD.

Foundation Support

国家自然科学基金资助项目(61562078,71563048)
新疆天山青年计划资助项目(2018Q073)
新疆高校研自科项目(XJEDU2021Y037)
新疆“天山雪松计划”青年拔尖人才计划资助项目

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.11.0639
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 6
Section: System Development & Application
Pages: 1796-1804
Serial Number: 1001-3695(2022)06-033-1796-09

Publish History

[2022-02-09] Accepted Paper
[2022-06-05] Printed Article

Cite This Article

陈小昆, 左航旭, 廖彬, 等. 融合XGBoost与SHAP的冠心病预测及其特征分析模型 [J]. 计算机应用研究, 2022, 39 (6): 1796-1804. (Chen Xiaokun, Zuo Hangxu, Liao Bin, et al. Coronary artery disease prediction and feature analysis model based on XGBoost and SHAP [J]. Application Research of Computers, 2022, 39 (6): 1796-1804. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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