Prediction of Masked Hypertension and Masked Uncontrolled Hypertension Using Machine Learning
Objective: This study aimed to develop machine learning-based prediction models to predict masked hypertension and masked uncontrolled hypertension using the clinical characteristics of patients at a single outpatient visit.Methods: Data were derived from two cohorts in Taiwan. The first cohort incl...
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Frontiers Media S.A.
2021
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oai:doaj.org-article:5fcaf9381f7549aa9f1bc8e4913787f72021-11-19T05:30:09ZPrediction of Masked Hypertension and Masked Uncontrolled Hypertension Using Machine Learning2297-055X10.3389/fcvm.2021.778306https://doaj.org/article/5fcaf9381f7549aa9f1bc8e4913787f72021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fcvm.2021.778306/fullhttps://doaj.org/toc/2297-055XObjective: This study aimed to develop machine learning-based prediction models to predict masked hypertension and masked uncontrolled hypertension using the clinical characteristics of patients at a single outpatient visit.Methods: Data were derived from two cohorts in Taiwan. The first cohort included 970 hypertensive patients recruited from six medical centers between 2004 and 2005, which were split into a training set (n = 679), a validation set (n = 146), and a test set (n = 145) for model development and internal validation. The second cohort included 416 hypertensive patients recruited from a single medical center between 2012 and 2020, which was used for external validation. We used 33 clinical characteristics as candidate variables to develop models based on logistic regression (LR), random forest (RF), eXtreme Gradient Boosting (XGboost), and artificial neural network (ANN).Results: The four models featured high sensitivity and high negative predictive value (NPV) in internal validation (sensitivity = 0.914–1.000; NPV = 0.853–1.000) and external validation (sensitivity = 0.950–1.000; NPV = 0.875–1.000). The RF, XGboost, and ANN models showed much higher area under the receiver operating characteristic curve (AUC) (0.799–0.851 in internal validation, 0.672–0.837 in external validation) than the LR model. Among the models, the RF model, composed of 6 predictor variables, had the best overall performance in both internal and external validation (AUC = 0.851 and 0.837; sensitivity = 1.000 and 1.000; specificity = 0.609 and 0.580; NPV = 1.000 and 1.000; accuracy = 0.766 and 0.721, respectively).Conclusion: An effective machine learning-based predictive model that requires data from a single clinic visit may help to identify masked hypertension and masked uncontrolled hypertension.Ming-Hui HungLing-Chieh ShihYu-Ching WangHsin-Bang LeuHsin-Bang LeuHsin-Bang LeuHsin-Bang LeuPo-Hsun HuangPo-Hsun HuangPo-Hsun HuangPo-Hsun HuangTao-Cheng WuTao-Cheng WuTao-Cheng WuShing-Jong LinShing-Jong LinShing-Jong LinShing-Jong LinWen-Harn PanJaw-Wen ChenJaw-Wen ChenJaw-Wen ChenJaw-Wen ChenChin-Chou HuangChin-Chou HuangChin-Chou HuangChin-Chou HuangFrontiers Media S.A.articleambulatory blood pressure monitoringartificial intelligencemachine learninghypertensionmasked hypertensionmasked uncontrolled hypertensionDiseases of the circulatory (Cardiovascular) systemRC666-701ENFrontiers in Cardiovascular Medicine, Vol 8 (2021) |
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DOAJ |
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EN |
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ambulatory blood pressure monitoring artificial intelligence machine learning hypertension masked hypertension masked uncontrolled hypertension Diseases of the circulatory (Cardiovascular) system RC666-701 |
| spellingShingle |
ambulatory blood pressure monitoring artificial intelligence machine learning hypertension masked hypertension masked uncontrolled hypertension Diseases of the circulatory (Cardiovascular) system RC666-701 Ming-Hui Hung Ling-Chieh Shih Yu-Ching Wang Hsin-Bang Leu Hsin-Bang Leu Hsin-Bang Leu Hsin-Bang Leu Po-Hsun Huang Po-Hsun Huang Po-Hsun Huang Po-Hsun Huang Tao-Cheng Wu Tao-Cheng Wu Tao-Cheng Wu Shing-Jong Lin Shing-Jong Lin Shing-Jong Lin Shing-Jong Lin Wen-Harn Pan Jaw-Wen Chen Jaw-Wen Chen Jaw-Wen Chen Jaw-Wen Chen Chin-Chou Huang Chin-Chou Huang Chin-Chou Huang Chin-Chou Huang Prediction of Masked Hypertension and Masked Uncontrolled Hypertension Using Machine Learning |
| description |
Objective: This study aimed to develop machine learning-based prediction models to predict masked hypertension and masked uncontrolled hypertension using the clinical characteristics of patients at a single outpatient visit.Methods: Data were derived from two cohorts in Taiwan. The first cohort included 970 hypertensive patients recruited from six medical centers between 2004 and 2005, which were split into a training set (n = 679), a validation set (n = 146), and a test set (n = 145) for model development and internal validation. The second cohort included 416 hypertensive patients recruited from a single medical center between 2012 and 2020, which was used for external validation. We used 33 clinical characteristics as candidate variables to develop models based on logistic regression (LR), random forest (RF), eXtreme Gradient Boosting (XGboost), and artificial neural network (ANN).Results: The four models featured high sensitivity and high negative predictive value (NPV) in internal validation (sensitivity = 0.914–1.000; NPV = 0.853–1.000) and external validation (sensitivity = 0.950–1.000; NPV = 0.875–1.000). The RF, XGboost, and ANN models showed much higher area under the receiver operating characteristic curve (AUC) (0.799–0.851 in internal validation, 0.672–0.837 in external validation) than the LR model. Among the models, the RF model, composed of 6 predictor variables, had the best overall performance in both internal and external validation (AUC = 0.851 and 0.837; sensitivity = 1.000 and 1.000; specificity = 0.609 and 0.580; NPV = 1.000 and 1.000; accuracy = 0.766 and 0.721, respectively).Conclusion: An effective machine learning-based predictive model that requires data from a single clinic visit may help to identify masked hypertension and masked uncontrolled hypertension. |
| format |
article |
| author |
Ming-Hui Hung Ling-Chieh Shih Yu-Ching Wang Hsin-Bang Leu Hsin-Bang Leu Hsin-Bang Leu Hsin-Bang Leu Po-Hsun Huang Po-Hsun Huang Po-Hsun Huang Po-Hsun Huang Tao-Cheng Wu Tao-Cheng Wu Tao-Cheng Wu Shing-Jong Lin Shing-Jong Lin Shing-Jong Lin Shing-Jong Lin Wen-Harn Pan Jaw-Wen Chen Jaw-Wen Chen Jaw-Wen Chen Jaw-Wen Chen Chin-Chou Huang Chin-Chou Huang Chin-Chou Huang Chin-Chou Huang |
| author_facet |
Ming-Hui Hung Ling-Chieh Shih Yu-Ching Wang Hsin-Bang Leu Hsin-Bang Leu Hsin-Bang Leu Hsin-Bang Leu Po-Hsun Huang Po-Hsun Huang Po-Hsun Huang Po-Hsun Huang Tao-Cheng Wu Tao-Cheng Wu Tao-Cheng Wu Shing-Jong Lin Shing-Jong Lin Shing-Jong Lin Shing-Jong Lin Wen-Harn Pan Jaw-Wen Chen Jaw-Wen Chen Jaw-Wen Chen Jaw-Wen Chen Chin-Chou Huang Chin-Chou Huang Chin-Chou Huang Chin-Chou Huang |
| author_sort |
Ming-Hui Hung |
| title |
Prediction of Masked Hypertension and Masked Uncontrolled Hypertension Using Machine Learning |
| title_short |
Prediction of Masked Hypertension and Masked Uncontrolled Hypertension Using Machine Learning |
| title_full |
Prediction of Masked Hypertension and Masked Uncontrolled Hypertension Using Machine Learning |
| title_fullStr |
Prediction of Masked Hypertension and Masked Uncontrolled Hypertension Using Machine Learning |
| title_full_unstemmed |
Prediction of Masked Hypertension and Masked Uncontrolled Hypertension Using Machine Learning |
| title_sort |
prediction of masked hypertension and masked uncontrolled hypertension using machine learning |
| publisher |
Frontiers Media S.A. |
| publishDate |
2021 |
| url |
https://doaj.org/article/5fcaf9381f7549aa9f1bc8e4913787f7 |
| work_keys_str_mv |
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| _version_ |
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