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Dynamic gestational week prediction model for pre-eclampsia based on ID3 algorithm
Pre-eclampsia (PE) is a type of hypertensive disorder during pregnancy, which is a serious threat to the life of mother and fetus. It is a placenta-derived disease that results in placental damage and necrosis due to systemic small vessel spasms that cause pathological changes such as ischemia and h...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9643850/ https://www.ncbi.nlm.nih.gov/pubmed/36388117 http://dx.doi.org/10.3389/fphys.2022.1035726 |
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author | Li, Ziwei Xu, Qi Sun, Ge Jia, Runqing Yang, Lin Liu, Guoli Hao, Dongmei Zhang, Song Yang, Yimin Li, Xuwen Zhang, Xinyu Lian, Cuiting |
author_facet | Li, Ziwei Xu, Qi Sun, Ge Jia, Runqing Yang, Lin Liu, Guoli Hao, Dongmei Zhang, Song Yang, Yimin Li, Xuwen Zhang, Xinyu Lian, Cuiting |
author_sort | Li, Ziwei |
collection | PubMed |
description | Pre-eclampsia (PE) is a type of hypertensive disorder during pregnancy, which is a serious threat to the life of mother and fetus. It is a placenta-derived disease that results in placental damage and necrosis due to systemic small vessel spasms that cause pathological changes such as ischemia and hypoxia and oxidative stress, which leads to fetal and maternal damage. In this study, four types of risk factors, namely, clinical epidemiology, hemodynamics, basic biochemistry, and biomarkers, were used for the initial selection of model parameters related to PE, and factors that were easily available and clinically recognized as being associated with a higher risk of PE were selected based on hospital medical record data. The model parameters were then further analyzed and screened in two subgroups: early-onset pre-eclampsia (EOPE) and late-onset pre-eclampsia (LOPE). Dynamic gestational week prediction model for PE using decision tree ID3 algorithm in machine learning. Performance of the model was: macro average (precision = 76%, recall = 73%, F1-score = 75%), weighted average (precision = 88%, recall = 89%, F1-score = 89%) and overall accuracy is 86%. In this study, the addition of the dynamic timeline parameter “gestational week” made the model more convenient for clinical application and achieved effective PE subgroup prediction. |
format | Online Article Text |
id | pubmed-9643850 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-96438502022-11-15 Dynamic gestational week prediction model for pre-eclampsia based on ID3 algorithm Li, Ziwei Xu, Qi Sun, Ge Jia, Runqing Yang, Lin Liu, Guoli Hao, Dongmei Zhang, Song Yang, Yimin Li, Xuwen Zhang, Xinyu Lian, Cuiting Front Physiol Physiology Pre-eclampsia (PE) is a type of hypertensive disorder during pregnancy, which is a serious threat to the life of mother and fetus. It is a placenta-derived disease that results in placental damage and necrosis due to systemic small vessel spasms that cause pathological changes such as ischemia and hypoxia and oxidative stress, which leads to fetal and maternal damage. In this study, four types of risk factors, namely, clinical epidemiology, hemodynamics, basic biochemistry, and biomarkers, were used for the initial selection of model parameters related to PE, and factors that were easily available and clinically recognized as being associated with a higher risk of PE were selected based on hospital medical record data. The model parameters were then further analyzed and screened in two subgroups: early-onset pre-eclampsia (EOPE) and late-onset pre-eclampsia (LOPE). Dynamic gestational week prediction model for PE using decision tree ID3 algorithm in machine learning. Performance of the model was: macro average (precision = 76%, recall = 73%, F1-score = 75%), weighted average (precision = 88%, recall = 89%, F1-score = 89%) and overall accuracy is 86%. In this study, the addition of the dynamic timeline parameter “gestational week” made the model more convenient for clinical application and achieved effective PE subgroup prediction. Frontiers Media S.A. 2022-10-26 /pmc/articles/PMC9643850/ /pubmed/36388117 http://dx.doi.org/10.3389/fphys.2022.1035726 Text en Copyright © 2022 Li, Xu, Sun, Jia, Yang, Liu, Hao, Zhang, Yang, Li, Zhang and Lian. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Physiology Li, Ziwei Xu, Qi Sun, Ge Jia, Runqing Yang, Lin Liu, Guoli Hao, Dongmei Zhang, Song Yang, Yimin Li, Xuwen Zhang, Xinyu Lian, Cuiting Dynamic gestational week prediction model for pre-eclampsia based on ID3 algorithm |
title | Dynamic gestational week prediction model for pre-eclampsia based on ID3 algorithm |
title_full | Dynamic gestational week prediction model for pre-eclampsia based on ID3 algorithm |
title_fullStr | Dynamic gestational week prediction model for pre-eclampsia based on ID3 algorithm |
title_full_unstemmed | Dynamic gestational week prediction model for pre-eclampsia based on ID3 algorithm |
title_short | Dynamic gestational week prediction model for pre-eclampsia based on ID3 algorithm |
title_sort | dynamic gestational week prediction model for pre-eclampsia based on id3 algorithm |
topic | Physiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9643850/ https://www.ncbi.nlm.nih.gov/pubmed/36388117 http://dx.doi.org/10.3389/fphys.2022.1035726 |
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