Utilizing machine learning algorithms for the prediction of carotid artery plaques in a Chinese population
Background: Ischemic stroke is a significant global health issue, imposing substantial social and economic burdens. Carotid artery plaques (CAP) serve as an important risk factor for stroke, and early screening can effectively reduce stroke incidence. However, China lacks nationwide data on carotid...
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Format: | Article |
Language: | English |
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Frontiers Media S.A.
2023-11-01
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Series: | Frontiers in Physiology |
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Online Access: | https://www.frontiersin.org/articles/10.3389/fphys.2023.1295371/full |
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author | Shuwei Weng Shuwei Weng Jin Chen Jin Chen Chen Ding Die Hu Die Hu Wenwu Liu Wenwu Liu Yanyi Yang Daoquan Peng Daoquan Peng |
author_facet | Shuwei Weng Shuwei Weng Jin Chen Jin Chen Chen Ding Die Hu Die Hu Wenwu Liu Wenwu Liu Yanyi Yang Daoquan Peng Daoquan Peng |
author_sort | Shuwei Weng |
collection | DOAJ |
description | Background: Ischemic stroke is a significant global health issue, imposing substantial social and economic burdens. Carotid artery plaques (CAP) serve as an important risk factor for stroke, and early screening can effectively reduce stroke incidence. However, China lacks nationwide data on carotid artery plaques. Machine learning (ML) can offer an economically efficient screening method. This study aimed to develop ML models using routine health examinations and blood markers to predict the occurrence of carotid artery plaques.Methods: This study included data from 5,211 participants aged 18–70, encompassing health check-ups and biochemical indicators. Among them, 1,164 participants were diagnosed with carotid artery plaques through carotid ultrasound. We constructed six ML models by employing feature selection with elastic net regression, selecting 13 indicators. Model performance was evaluated using accuracy, sensitivity, specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV), F1 score, kappa value, and Area Under the Curve (AUC) value. Feature importance was assessed by calculating the root mean square error (RMSE) loss after permutations for each variable in every model.Results: Among all six ML models, LightGBM achieved the highest accuracy at 91.8%. Feature importance analysis revealed that age, Low-Density Lipoprotein Cholesterol (LDL-c), and systolic blood pressure were important predictive factors in the models.Conclusion: LightGBM can effectively predict the occurrence of carotid artery plaques using demographic information, physical examination data and biochemistry data. |
first_indexed | 2024-03-11T12:24:16Z |
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institution | Directory Open Access Journal |
issn | 1664-042X |
language | English |
last_indexed | 2024-03-11T12:24:16Z |
publishDate | 2023-11-01 |
publisher | Frontiers Media S.A. |
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series | Frontiers in Physiology |
spelling | doaj.art-e87764e918c74fc69943caa6410215952023-11-06T15:52:46ZengFrontiers Media S.A.Frontiers in Physiology1664-042X2023-11-011410.3389/fphys.2023.12953711295371Utilizing machine learning algorithms for the prediction of carotid artery plaques in a Chinese populationShuwei Weng0Shuwei Weng1Jin Chen2Jin Chen3Chen Ding4Die Hu5Die Hu6Wenwu Liu7Wenwu Liu8Yanyi Yang9Daoquan Peng10Daoquan Peng11Department of Cardiovascular Medicine, The Second Xiangya Hospital, Central South University, Changsha, Hunan, ChinaResearch Institute of Blood Lipid and Atherosclerosis, Changsha, Hunan, ChinaDepartment of Cardiovascular Medicine, The Second Xiangya Hospital, Central South University, Changsha, Hunan, ChinaResearch Institute of Blood Lipid and Atherosclerosis, Changsha, Hunan, ChinaDepartment of Cardiology, Suzhou Dushu Lake Hospital, Dushu Lake Hospital Affiliated to Soochow University, Medical Center of Soochow University, Suzhou, Jiangsu, ChinaDepartment of Cardiovascular Medicine, The Second Xiangya Hospital, Central South University, Changsha, Hunan, ChinaResearch Institute of Blood Lipid and Atherosclerosis, Changsha, Hunan, ChinaDepartment of Cardiovascular Medicine, The Second Xiangya Hospital, Central South University, Changsha, Hunan, ChinaResearch Institute of Blood Lipid and Atherosclerosis, Changsha, Hunan, ChinaHealth Management Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan, ChinaDepartment of Cardiovascular Medicine, The Second Xiangya Hospital, Central South University, Changsha, Hunan, ChinaResearch Institute of Blood Lipid and Atherosclerosis, Changsha, Hunan, ChinaBackground: Ischemic stroke is a significant global health issue, imposing substantial social and economic burdens. Carotid artery plaques (CAP) serve as an important risk factor for stroke, and early screening can effectively reduce stroke incidence. However, China lacks nationwide data on carotid artery plaques. Machine learning (ML) can offer an economically efficient screening method. This study aimed to develop ML models using routine health examinations and blood markers to predict the occurrence of carotid artery plaques.Methods: This study included data from 5,211 participants aged 18–70, encompassing health check-ups and biochemical indicators. Among them, 1,164 participants were diagnosed with carotid artery plaques through carotid ultrasound. We constructed six ML models by employing feature selection with elastic net regression, selecting 13 indicators. Model performance was evaluated using accuracy, sensitivity, specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV), F1 score, kappa value, and Area Under the Curve (AUC) value. Feature importance was assessed by calculating the root mean square error (RMSE) loss after permutations for each variable in every model.Results: Among all six ML models, LightGBM achieved the highest accuracy at 91.8%. Feature importance analysis revealed that age, Low-Density Lipoprotein Cholesterol (LDL-c), and systolic blood pressure were important predictive factors in the models.Conclusion: LightGBM can effectively predict the occurrence of carotid artery plaques using demographic information, physical examination data and biochemistry data.https://www.frontiersin.org/articles/10.3389/fphys.2023.1295371/fullcarotid artery plaquemachine learningLightGBMscreeningprimary prevention |
spellingShingle | Shuwei Weng Shuwei Weng Jin Chen Jin Chen Chen Ding Die Hu Die Hu Wenwu Liu Wenwu Liu Yanyi Yang Daoquan Peng Daoquan Peng Utilizing machine learning algorithms for the prediction of carotid artery plaques in a Chinese population Frontiers in Physiology carotid artery plaque machine learning LightGBM screening primary prevention |
title | Utilizing machine learning algorithms for the prediction of carotid artery plaques in a Chinese population |
title_full | Utilizing machine learning algorithms for the prediction of carotid artery plaques in a Chinese population |
title_fullStr | Utilizing machine learning algorithms for the prediction of carotid artery plaques in a Chinese population |
title_full_unstemmed | Utilizing machine learning algorithms for the prediction of carotid artery plaques in a Chinese population |
title_short | Utilizing machine learning algorithms for the prediction of carotid artery plaques in a Chinese population |
title_sort | utilizing machine learning algorithms for the prediction of carotid artery plaques in a chinese population |
topic | carotid artery plaque machine learning LightGBM screening primary prevention |
url | https://www.frontiersin.org/articles/10.3389/fphys.2023.1295371/full |
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