Volume 20, Issue 1 (Vol.20, No.1, Spring 2024)
irje 2024, 20(1): 1-14 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Farrokhi R, Hosseinzadeh S, Habibelahi A, Biglarian A. Predicting the Occurrence of Preterm Birth and Determining its Risk Factors Individually Using an Interpretable Machine Learning Model. irje 2024; 20 (1) :1-14
URL: http://irje.tums.ac.ir/article-1-7318-en.html
URL: http://irje.tums.ac.ir/article-1-7318-en.html
1- MSc. Student in Biostatistics, Department of Biostatistics and Epidemiology, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran
2- Assistant Professor of Biostatistics, Department of Biostatistics and Epidemiology, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran
3- Assistant Professor of Pediatrics, Vice Chancellery for Health, Iran Ministry of Health and Medical Education, Tehran, Iran
4- Professor of Biostatistics, Department of Biostatistics and Epidemiology, Social Determinants of Health Research Center, Social Health Research Institute, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran ,abiglaria@uswr.ac.ir
2- Assistant Professor of Biostatistics, Department of Biostatistics and Epidemiology, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran
3- Assistant Professor of Pediatrics, Vice Chancellery for Health, Iran Ministry of Health and Medical Education, Tehran, Iran
4- Professor of Biostatistics, Department of Biostatistics and Epidemiology, Social Determinants of Health Research Center, Social Health Research Institute, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran ,
Abstract: (286 Views)
Background and Objectives: Identifying pregnant women who are at risk of premature birth and determining its risk factors is essential because it affects their health. This study aimed to use an interpretable machine-learning model to predict premature birth.
Methods: In this study, data from 149,350 births in Tehran in 2019 were utilized from the Iranian Mothers and Babies Network (IMaN) dataset. Various factors related to the mother and the fetus, such as the mother's demographic variables and health status, medical history, pregnancy conditions, childbirth, and associated risks, were considered. The machine learning models, including multilayer neural networks, random forest, and XGBoost, were employed to predict the occurrence of preterm birth after data preprocessing. The models were evaluated based on accuracy, sensitivity, specificity, and area under the ROC curve. The Python programming language version 3.10.0 was applied to analyze the data.
Results: About 8.67% of births were premature. The XGBoost algorithm achieved the highest prediction accuracy (90%). According to the model output, multiple births, which account for 46% of pregnant women's births, had the highest importance score. Delivery risk factors had a score of 41%, and other variables, including neurological and mental illness, preeclampsia, and cardiovascular disease, were subsequently ranked in order of importance for this particular individual.
Conclusion: Using an interpretable machine learning method could predict the occurrence of premature birth. Based on risk factors, the interpretable machine learning method can provide personalized preventive recommendations for every pregnant woman, aiming to reduce the risk of preterm birth.
Methods: In this study, data from 149,350 births in Tehran in 2019 were utilized from the Iranian Mothers and Babies Network (IMaN) dataset. Various factors related to the mother and the fetus, such as the mother's demographic variables and health status, medical history, pregnancy conditions, childbirth, and associated risks, were considered. The machine learning models, including multilayer neural networks, random forest, and XGBoost, were employed to predict the occurrence of preterm birth after data preprocessing. The models were evaluated based on accuracy, sensitivity, specificity, and area under the ROC curve. The Python programming language version 3.10.0 was applied to analyze the data.
Results: About 8.67% of births were premature. The XGBoost algorithm achieved the highest prediction accuracy (90%). According to the model output, multiple births, which account for 46% of pregnant women's births, had the highest importance score. Delivery risk factors had a score of 41%, and other variables, including neurological and mental illness, preeclampsia, and cardiovascular disease, were subsequently ranked in order of importance for this particular individual.
Conclusion: Using an interpretable machine learning method could predict the occurrence of premature birth. Based on risk factors, the interpretable machine learning method can provide personalized preventive recommendations for every pregnant woman, aiming to reduce the risk of preterm birth.
Type of Study: Research |
Subject:
Special
Received: 2023/12/27 | Accepted: 2024/04/24 | Published: 2024/06/12
Received: 2023/12/27 | Accepted: 2024/04/24 | Published: 2024/06/12
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
