Prediction of cardiovascular disease risk based on major contributing features
Abstract The risk of cardiovascular disease (CVD) is a serious health threat to human society worldwide. The use of machine learning methods to predict the risk of CVD is of great relevance to identify high-risk patients and take timely interventions. In this study, we propose the XGBH machine learn...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-03-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-31870-8 |
| _version_ | 1797859935009112064 |
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| author | Mengxiao Peng Fan Hou Zhixiang Cheng Tongtong Shen Kaixian Liu Cai Zhao Wen Zheng |
| author_facet | Mengxiao Peng Fan Hou Zhixiang Cheng Tongtong Shen Kaixian Liu Cai Zhao Wen Zheng |
| author_sort | Mengxiao Peng |
| collection | DOAJ |
| description | Abstract The risk of cardiovascular disease (CVD) is a serious health threat to human society worldwide. The use of machine learning methods to predict the risk of CVD is of great relevance to identify high-risk patients and take timely interventions. In this study, we propose the XGBH machine learning model, which is a CVD risk prediction model based on key contributing features. In this paper, the generalisation of the model was enhanced by adding retrospective data of 14,832 Chinese Shanxi CVD patients to the kaggle dataset. The XGBH risk prediction model proposed in this paper was validated to be highly accurate (AUC = 0.81) compared to the baseline risk score (AUC = 0.65), and the accuracy of the model for CVD risk prediction was improved with the inclusion of the conventional biometric BMI variable. To increase the clinical application of the model, a simpler diagnostic model was designed in this paper, which requires only three characteristics from the patient (age, value of systolic blood pressure and whether cholesterol is normal or not) to enable early intervention in the treatment of high-risk patients with a slight reduction in accuracy (AUC = 0.79). Ultimately, a CVD risk score model with few features and high accuracy will be established based on the main contributing features. Of course, further prospective studies, as well as studies with other populations, are needed to assess the actual clinical effectiveness of the XGBH risk prediction model. |
| first_indexed | 2024-04-09T21:37:36Z |
| format | Article |
| id | doaj.art-1f910b86d1dc418d9f7239eea2a9ccd0 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-09T21:37:36Z |
| publishDate | 2023-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-1f910b86d1dc418d9f7239eea2a9ccd02023-03-26T11:10:48ZengNature PortfolioScientific Reports2045-23222023-03-0113111110.1038/s41598-023-31870-8Prediction of cardiovascular disease risk based on major contributing featuresMengxiao Peng0Fan Hou1Zhixiang Cheng2Tongtong Shen3Kaixian Liu4Cai Zhao5Wen Zheng6Institute of Public-Safety and Big Data, College of Data Science, Taiyuan University of TechnologyInstitute of Public-Safety and Big Data, College of Data Science, Taiyuan University of TechnologyInstitute of Public-Safety and Big Data, College of Data Science, Taiyuan University of TechnologyInstitute of Public-Safety and Big Data, College of Data Science, Taiyuan University of TechnologyInstitute of Public-Safety and Big Data, College of Data Science, Taiyuan University of TechnologyInstitute of Public-Safety and Big Data, College of Data Science, Taiyuan University of TechnologyInstitute of Public-Safety and Big Data, College of Data Science, Taiyuan University of TechnologyAbstract The risk of cardiovascular disease (CVD) is a serious health threat to human society worldwide. The use of machine learning methods to predict the risk of CVD is of great relevance to identify high-risk patients and take timely interventions. In this study, we propose the XGBH machine learning model, which is a CVD risk prediction model based on key contributing features. In this paper, the generalisation of the model was enhanced by adding retrospective data of 14,832 Chinese Shanxi CVD patients to the kaggle dataset. The XGBH risk prediction model proposed in this paper was validated to be highly accurate (AUC = 0.81) compared to the baseline risk score (AUC = 0.65), and the accuracy of the model for CVD risk prediction was improved with the inclusion of the conventional biometric BMI variable. To increase the clinical application of the model, a simpler diagnostic model was designed in this paper, which requires only three characteristics from the patient (age, value of systolic blood pressure and whether cholesterol is normal or not) to enable early intervention in the treatment of high-risk patients with a slight reduction in accuracy (AUC = 0.79). Ultimately, a CVD risk score model with few features and high accuracy will be established based on the main contributing features. Of course, further prospective studies, as well as studies with other populations, are needed to assess the actual clinical effectiveness of the XGBH risk prediction model.https://doi.org/10.1038/s41598-023-31870-8 |
| spellingShingle | Mengxiao Peng Fan Hou Zhixiang Cheng Tongtong Shen Kaixian Liu Cai Zhao Wen Zheng Prediction of cardiovascular disease risk based on major contributing features Scientific Reports |
| title | Prediction of cardiovascular disease risk based on major contributing features |
| title_full | Prediction of cardiovascular disease risk based on major contributing features |
| title_fullStr | Prediction of cardiovascular disease risk based on major contributing features |
| title_full_unstemmed | Prediction of cardiovascular disease risk based on major contributing features |
| title_short | Prediction of cardiovascular disease risk based on major contributing features |
| title_sort | prediction of cardiovascular disease risk based on major contributing features |
| url | https://doi.org/10.1038/s41598-023-31870-8 |
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