Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Li, Ziwei, Xu, Qi, Sun, Ge, Jia, Runqing, Yang, Lin, Liu, Guoli, Hao, Dongmei, Zhang, Song, Yang, Yimin, Li, Xuwen, Zhang, Xinyu, Lian, Cuiting
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
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
_version_ 1784826610883493888
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
work_keys_str_mv AT liziwei dynamicgestationalweekpredictionmodelforpreeclampsiabasedonid3algorithm
AT xuqi dynamicgestationalweekpredictionmodelforpreeclampsiabasedonid3algorithm
AT sunge dynamicgestationalweekpredictionmodelforpreeclampsiabasedonid3algorithm
AT jiarunqing dynamicgestationalweekpredictionmodelforpreeclampsiabasedonid3algorithm
AT yanglin dynamicgestationalweekpredictionmodelforpreeclampsiabasedonid3algorithm
AT liuguoli dynamicgestationalweekpredictionmodelforpreeclampsiabasedonid3algorithm
AT haodongmei dynamicgestationalweekpredictionmodelforpreeclampsiabasedonid3algorithm
AT zhangsong dynamicgestationalweekpredictionmodelforpreeclampsiabasedonid3algorithm
AT yangyimin dynamicgestationalweekpredictionmodelforpreeclampsiabasedonid3algorithm
AT lixuwen dynamicgestationalweekpredictionmodelforpreeclampsiabasedonid3algorithm
AT zhangxinyu dynamicgestationalweekpredictionmodelforpreeclampsiabasedonid3algorithm
AT liancuiting dynamicgestationalweekpredictionmodelforpreeclampsiabasedonid3algorithm