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Wang J, Xu Y, Liu L, Wu W, Shen C, Huang H, Zhen Z, Meng J, Li C, Qu Z, He Q, Tian Y. Comparison of LASSO and random forest models for predicting the risk of premature coronary artery disease. BMC Med Inform Decis Mak 2023; 23:297. [PMID: 38124036 PMCID: PMC10734117 DOI: 10.1186/s12911-023-02407-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Accepted: 12/14/2023] [Indexed: 12/23/2023] Open
Abstract
PURPOSE With the change of lifestyle, the occurrence of coronary artery disease presents a younger trend, increasing the medical and economic burden on the family and society. To reduce the burden caused by this disease, this study applied LASSO Logistic Regression and Random Forest to establish a risk prediction model for premature coronary artery disease(PCAD) separately and compared the predictive performance of the two models. METHODS The data are obtained from 1004 patients with coronary artery disease admitted to a third-class hospital in Liaoning Province from September 2019 to December 2021. The data from 797 patients were ultimately evaluated. The dataset of 797 patients was randomly divided into the training set (569 persons) and the validation set (228 persons) scale by 7:3. The risk prediction model was established and compared by LASSO Logistic and Random Forest. RESULT The two models in this study showed that hyperuricemia, chronic renal disease, carotid artery atherosclerosis were important predictors of premature coronary artery disease. A result of the AUC between the two models showed statistical difference (Z = 3.47, P < 0.05). CONCLUSIONS Random Forest has better prediction performance for PCAD and is suitable for clinical practice. It can provide an objective reference for the early screening and diagnosis of premature coronary artery disease, guide clinical decision-making and promote disease prevention.
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Affiliation(s)
- Jiayu Wang
- School of Nursing, Liaoning University of Traditional Chinese Medicine, 110847, Shenyang, China
| | - Yikang Xu
- Department of Cardiovascular Medicine, The Second Affiliated Hospital of Shenyang Medical College, 110002, Shenyang, China.
| | - Lei Liu
- School of Nursing, Liaoning University of Traditional Chinese Medicine, 110847, Shenyang, China
| | - Wei Wu
- Institute of Humanities and Social Sciences, Shenyang University, 110044, Shenyang, China
| | - Chunjian Shen
- Department of Cardiac Surgery, The Second Affiliated Hospital of Shenyang Medical College, 110002, Shenyang, China
| | - Henan Huang
- Library, Shenyang Medical College, 110034, Shenyang, China
| | - Ziyi Zhen
- School of Public Health, Shenyang medical college, 110034, Shenyang, China
| | - Jixian Meng
- School of nursing, Liaoning Jinqiu Hospital, 110034, Shenyang, China
| | - Chunjing Li
- School of nursing, The First Affiliated Hospital of China Medical University, 110034, Shenyang, China
| | - Zhixin Qu
- School of nursing, Shenyang medical college, 110034, Shenyang, China
| | - Qinglei He
- School of Nursing, Liaoning University of Traditional Chinese Medicine, 110847, Shenyang, China
| | - Yu Tian
- School of Nursing, Liaoning University of Traditional Chinese Medicine, 110847, Shenyang, China
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Guo Y, Xia C, Zhong Y, Wei Y, Zhu H, Ma J, Li G, Meng X, Yang C, Wang X, Wang F. Machine learning-enhanced echocardiography for screening coronary artery disease. Biomed Eng Online 2023; 22:44. [PMID: 37170232 PMCID: PMC10176743 DOI: 10.1186/s12938-023-01106-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Accepted: 04/25/2023] [Indexed: 05/13/2023] Open
Abstract
BACKGROUND Since myocardial work (MW) and left atrial strain are valuable for screening coronary artery disease (CAD), this study aimed to develop a novel CAD screening approach based on machine learning-enhanced echocardiography. METHODS This prospective study used data from patients undergoing coronary angiography, in which the novel echocardiography features were extracted by a machine learning algorithm. A total of 818 patients were enrolled and randomly divided into training (80%) and testing (20%) groups. An additional 115 patients were also enrolled in the validation group. RESULTS The superior diagnosis model of CAD was optimized using 59 echocardiographic features in a gradient-boosting classifier. This model showed that the value of the receiver operating characteristic area under the curve (AUC) was 0.852 in the test group and 0.834 in the validation group, with high sensitivity (0.952) and low specificity (0.691), suggesting that this model is very sensitive for detecting CAD, but its low specificity may increase the high false-positive rate. We also determined that the false-positive cases were more susceptible to suffering cardiac events than the true-negative cases. CONCLUSIONS Machine learning-enhanced echocardiography can improve CAD detection based on the MW and left atrial strain features. Our developed model is valuable for estimating the pre-test probability of CAD and screening CAD patients in clinical practice. TRIAL REGISTRATION Registered as NCT03905200 at ClinicalTrials.gov. Registered on 5 April 2019.
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Affiliation(s)
- Ying Guo
- Department of Cardiology, Beijing Hospital, National Center of Gerontology; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, People's Republic of China
| | - Chenxi Xia
- Department of Cardiology, Beijing Hospital, National Center of Gerontology; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, People's Republic of China
| | - You Zhong
- Department of Cardiology, Beijing Hospital, National Center of Gerontology; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, People's Republic of China
| | - Yiliang Wei
- Jiangsu Key Laboratory of Phylogenomics and Comparative Genomics, School of Life Sciences, Jiangsu Normal University, Xuzhou, 221116, Jiangsu, People's Republic of China
- Department of Immunology, Biochemistry and Molecular Biology, 2011 Collaborative Innovation Center of Tianjin for Medical Epigenetics, Tianjin Key Laboratory of Medical Epigenetics, Tianjin Medical University, Tianjin, 300070, People's Republic of China
| | - Huolan Zhu
- Department of Gerontology, Shaanxi Provincial People's Hospital, Shaanxi Provincial Clinical Research Center for Geriatric Medicine, No. 256 Youyi West Road, Xi'an, China
| | - Jianqiang Ma
- Keya Medical Technology Co., Ltd, Beijing, People's Republic of China
| | - Guang Li
- Keya Medical Technology Co., Ltd, Beijing, People's Republic of China
| | - Xuyang Meng
- Department of Cardiology, Beijing Hospital, National Center of Gerontology; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, People's Republic of China
| | - Chenguang Yang
- Department of Cardiology, Beijing Hospital, National Center of Gerontology; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, People's Republic of China
| | - Xiang Wang
- Department of Cardiology, Beijing Hospital, National Center of Gerontology; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, People's Republic of China.
| | - Fang Wang
- Department of Cardiology, Beijing Hospital, National Center of Gerontology; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, People's Republic of China.
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