Prediction of severe preeclampsia in machine learning
This study aimed to find out the blood data characteristics of patients and explore the correlation between severe preeclampsia and blood index value. Provide assistance for the early attention direction of severe preeclampsia diagnosis and treatment. 19,653 pregnant women presenting to the West Chi...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-09-01
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| Series: | Medicine in Novel Technology and Devices |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590093522000455 |
| _version_ | 1811215942013681664 |
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| author | Xinyuan Zhang Yu Chen Stephen Salerno Yi Li Libin Zhou Xiaoxi Zeng Huafeng Li |
| author_facet | Xinyuan Zhang Yu Chen Stephen Salerno Yi Li Libin Zhou Xiaoxi Zeng Huafeng Li |
| author_sort | Xinyuan Zhang |
| collection | DOAJ |
| description | This study aimed to find out the blood data characteristics of patients and explore the correlation between severe preeclampsia and blood index value. Provide assistance for the early attention direction of severe preeclampsia diagnosis and treatment. 19,653 pregnant women presenting to the West China Second University Hospital, Sichuan University from January 2017 to April 2019. After screening, a total of 248 patients, 124 severe preeclampsia cases, and 124 controls were selected for this study. Forty-three blood examination variables were obtained from routine blood work, hepatic, renal and coagulation function examination. Light gradient boosting machine (light GBM), decision tree and random forest were used for date diving. We randomly divided 35% of the original data as a testing set to conduct internal validation of the performance of the prediction model. The area under receiver operating characteristic curve (AUC) was used as the main score to compare the three methods. Finally, a binary classification light GBM model based on aspartate aminotransferase, direct bilirubin and activated partial thromboplastin time ratio can predict severe preeclampsia with sensitivity of 88.37%, specificity of 77.27%, AUC of 89.74% and positive predictive value of 65.96%. We believe relevant quantifiable indicators can establish an effective prediction model, which can provide guidance for early detection and prevention of severe preeclampsia. |
| first_indexed | 2024-04-12T06:30:44Z |
| format | Article |
| id | doaj.art-d8b06d0d6bb04bf88163ae3d1dd3d4b5 |
| institution | Directory Open Access Journal |
| issn | 2590-0935 |
| language | English |
| last_indexed | 2024-04-12T06:30:44Z |
| publishDate | 2022-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Medicine in Novel Technology and Devices |
| spelling | doaj.art-d8b06d0d6bb04bf88163ae3d1dd3d4b52022-12-22T03:44:01ZengElsevierMedicine in Novel Technology and Devices2590-09352022-09-0115100158Prediction of severe preeclampsia in machine learningXinyuan Zhang0Yu Chen1Stephen Salerno2Yi Li3Libin Zhou4Xiaoxi Zeng5Huafeng Li6Department of Applied Mechanics, College of Architecture and Environment, Sichuan Province Biomechanical Engineering Laboratory, Sichuan University, Chengdu, 610065, Sichuan, ChinaDepartment of Applied Mechanics, College of Architecture and Environment, Sichuan Province Biomechanical Engineering Laboratory, Sichuan University, Chengdu, 610065, Sichuan, China; Medical Big Data Center, Sichuan University, Chengdu, 610065, Sichuan, China; Corresponding author. Department of Applied Mechanics, Sichuan University, Chengdu, 610065, Sichuan, China.Department of Biostatistics, University of Michigan, Ann Arbor, MI, USADepartment of Biostatistics, University of Michigan, Ann Arbor, MI, USADepartment of Computer Science, University of Wisconsin, Madison, WI, USAMedical Big Data Center, Sichuan University, Chengdu, 610065, Sichuan, ChinaWest China Second University Hospital, Sichuan University, Chengdu, 610041, China; Corresponding author.This study aimed to find out the blood data characteristics of patients and explore the correlation between severe preeclampsia and blood index value. Provide assistance for the early attention direction of severe preeclampsia diagnosis and treatment. 19,653 pregnant women presenting to the West China Second University Hospital, Sichuan University from January 2017 to April 2019. After screening, a total of 248 patients, 124 severe preeclampsia cases, and 124 controls were selected for this study. Forty-three blood examination variables were obtained from routine blood work, hepatic, renal and coagulation function examination. Light gradient boosting machine (light GBM), decision tree and random forest were used for date diving. We randomly divided 35% of the original data as a testing set to conduct internal validation of the performance of the prediction model. The area under receiver operating characteristic curve (AUC) was used as the main score to compare the three methods. Finally, a binary classification light GBM model based on aspartate aminotransferase, direct bilirubin and activated partial thromboplastin time ratio can predict severe preeclampsia with sensitivity of 88.37%, specificity of 77.27%, AUC of 89.74% and positive predictive value of 65.96%. We believe relevant quantifiable indicators can establish an effective prediction model, which can provide guidance for early detection and prevention of severe preeclampsia.http://www.sciencedirect.com/science/article/pii/S2590093522000455PreeclampsiaScreeningPredictionBlood examinationData characteristics |
| spellingShingle | Xinyuan Zhang Yu Chen Stephen Salerno Yi Li Libin Zhou Xiaoxi Zeng Huafeng Li Prediction of severe preeclampsia in machine learning Medicine in Novel Technology and Devices Preeclampsia Screening Prediction Blood examination Data characteristics |
| title | Prediction of severe preeclampsia in machine learning |
| title_full | Prediction of severe preeclampsia in machine learning |
| title_fullStr | Prediction of severe preeclampsia in machine learning |
| title_full_unstemmed | Prediction of severe preeclampsia in machine learning |
| title_short | Prediction of severe preeclampsia in machine learning |
| title_sort | prediction of severe preeclampsia in machine learning |
| topic | Preeclampsia Screening Prediction Blood examination Data characteristics |
| url | http://www.sciencedirect.com/science/article/pii/S2590093522000455 |
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