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Machine-learning-based analysis of the sensitivity and specificity on lipid-lowering effect of one-month-administered statins
Few predictive studies have been reported on the efficacy of atorvastatin in reducing lipoprotein cholesterol to be qualified after 1-month course of treatment in different individuals. A total of 14,180 community-based residents aged ≥ 65 received health checkup, 1013 of whom had low-density lipopr...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Lippincott Williams & Wilkins
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9981436/ https://www.ncbi.nlm.nih.gov/pubmed/36862920 http://dx.doi.org/10.1097/MD.0000000000033139 |
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author | Liu, Huiqin Jiao, Ronghong Wang, Lingling Feng, Fei Zhao, Xiaohui Yang, Juan |
author_facet | Liu, Huiqin Jiao, Ronghong Wang, Lingling Feng, Fei Zhao, Xiaohui Yang, Juan |
author_sort | Liu, Huiqin |
collection | PubMed |
description | Few predictive studies have been reported on the efficacy of atorvastatin in reducing lipoprotein cholesterol to be qualified after 1-month course of treatment in different individuals. A total of 14,180 community-based residents aged ≥ 65 received health checkup, 1013 of whom had low-density lipoprotein (LDL) higher than 2.6mmol/L so that they were put on 1-month course of treatment with atorvastatin. At its completion, lipoprotein cholesterol was measured again. With < 2.6 mmol/L considered as the treatment standard, 411 individuals were judged as the qualified group, and 602, and as the unqualified group. The basic sociodemographic features covered 57 items. The data were randomly divided into train sets and test ones. The recursive random-forest algorithm was applied to predicting the patients response to atorvastatin, the recursive feature elimination method, to screening all the physical indicators. The overall accuracy, sensitivity and specificity were calculated, respectively, and so were the receiver operator characteristic curve and the area under the curve of the test set. In the prediction model on the efficacy of 1-month treatment of statins for LDL, the sensitivity, 86.86%; and the specificity, 94.83%. In the prediction model on the efficacy of the same treatment for triglyceride, the sensitivity, 71.21%; and the specificity, 73.46%. As to the prediction of total cholesterol, the sensitivity, 94.38%; and the specificity, 96.55%. And in the case of high-density lipoprotein (HDL), the sensitivity, 84.86%; and the specificity, 100%. recursive feature elimination analysis showed that total cholesterol was the most important feature of atorvastatin efficacy of reducing LDL; that HDL was the most important one of its efficacies of reducing triglycerides; that LDL was the most important one of its efficacies of reducing total cholesterol; and that triglyceride was the most important one of its efficacies of reducing HDL. Random-forest can help predict whether atorvastatin efficacy of reducing lipoprotein cholesterol to be qualified after 1-month course of treatment in different individuals. |
format | Online Article Text |
id | pubmed-9981436 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Lippincott Williams & Wilkins |
record_format | MEDLINE/PubMed |
spelling | pubmed-99814362023-03-04 Machine-learning-based analysis of the sensitivity and specificity on lipid-lowering effect of one-month-administered statins Liu, Huiqin Jiao, Ronghong Wang, Lingling Feng, Fei Zhao, Xiaohui Yang, Juan Medicine (Baltimore) 4200 Few predictive studies have been reported on the efficacy of atorvastatin in reducing lipoprotein cholesterol to be qualified after 1-month course of treatment in different individuals. A total of 14,180 community-based residents aged ≥ 65 received health checkup, 1013 of whom had low-density lipoprotein (LDL) higher than 2.6mmol/L so that they were put on 1-month course of treatment with atorvastatin. At its completion, lipoprotein cholesterol was measured again. With < 2.6 mmol/L considered as the treatment standard, 411 individuals were judged as the qualified group, and 602, and as the unqualified group. The basic sociodemographic features covered 57 items. The data were randomly divided into train sets and test ones. The recursive random-forest algorithm was applied to predicting the patients response to atorvastatin, the recursive feature elimination method, to screening all the physical indicators. The overall accuracy, sensitivity and specificity were calculated, respectively, and so were the receiver operator characteristic curve and the area under the curve of the test set. In the prediction model on the efficacy of 1-month treatment of statins for LDL, the sensitivity, 86.86%; and the specificity, 94.83%. In the prediction model on the efficacy of the same treatment for triglyceride, the sensitivity, 71.21%; and the specificity, 73.46%. As to the prediction of total cholesterol, the sensitivity, 94.38%; and the specificity, 96.55%. And in the case of high-density lipoprotein (HDL), the sensitivity, 84.86%; and the specificity, 100%. recursive feature elimination analysis showed that total cholesterol was the most important feature of atorvastatin efficacy of reducing LDL; that HDL was the most important one of its efficacies of reducing triglycerides; that LDL was the most important one of its efficacies of reducing total cholesterol; and that triglyceride was the most important one of its efficacies of reducing HDL. Random-forest can help predict whether atorvastatin efficacy of reducing lipoprotein cholesterol to be qualified after 1-month course of treatment in different individuals. Lippincott Williams & Wilkins 2023-03-03 /pmc/articles/PMC9981436/ /pubmed/36862920 http://dx.doi.org/10.1097/MD.0000000000033139 Text en Copyright © 2023 the Author(s). Published by Wolters Kluwer Health, Inc. https://creativecommons.org/licenses/by-nc/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC) (https://creativecommons.org/licenses/by-nc/4.0/) , where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal. |
spellingShingle | 4200 Liu, Huiqin Jiao, Ronghong Wang, Lingling Feng, Fei Zhao, Xiaohui Yang, Juan Machine-learning-based analysis of the sensitivity and specificity on lipid-lowering effect of one-month-administered statins |
title | Machine-learning-based analysis of the sensitivity and specificity on lipid-lowering effect of one-month-administered statins |
title_full | Machine-learning-based analysis of the sensitivity and specificity on lipid-lowering effect of one-month-administered statins |
title_fullStr | Machine-learning-based analysis of the sensitivity and specificity on lipid-lowering effect of one-month-administered statins |
title_full_unstemmed | Machine-learning-based analysis of the sensitivity and specificity on lipid-lowering effect of one-month-administered statins |
title_short | Machine-learning-based analysis of the sensitivity and specificity on lipid-lowering effect of one-month-administered statins |
title_sort | machine-learning-based analysis of the sensitivity and specificity on lipid-lowering effect of one-month-administered statins |
topic | 4200 |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9981436/ https://www.ncbi.nlm.nih.gov/pubmed/36862920 http://dx.doi.org/10.1097/MD.0000000000033139 |
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