Associations of Preterm Birth with Dental and Gastrointestinal Diseases: Machine Learning Analysis Using National Health Insurance Data

In-Seok Song, Eun-Saem Choi, Eun Sun Kim, Yujin Hwang, Kwang-Sig Lee () and Ki Hoon Ahn ()
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In-Seok Song: Department of Oral and Maxillofacial Surgery, Korea University College of Medicine, Korea University Anam Hospital, Seoul 02841, Republic of Korea
Eun-Saem Choi: Department of Obstetrics and Gynecology, Korea University College of Medicine, Korea University Anam Hospital, Seoul 02841, Republic of Korea
Eun Sun Kim: Department of Gastroenterology, Korea University College of Medicine, Korea University Anam Hospital, Seoul 02841, Republic of Korea
Yujin Hwang: Department of Statistics, Korea University College of Political Science & Economics, Korea University Anam Hospital, Seoul 02841, Republic of Korea
Kwang-Sig Lee: AI Center, Korea University College of Medicine, Korea University Anam Hospital, Seoul 02841, Republic of Korea
Ki Hoon Ahn: Department of Obstetrics and Gynecology, Korea University College of Medicine, Korea University Anam Hospital, Seoul 02841, Republic of Korea

IJERPH, 2023, vol. 20, issue 3, 1-9

Abstract: Background: This study uses machine learning with large-scale population data to assess the associations of preterm birth (PTB) with dental and gastrointestinal diseases. Methods: Population-based retrospective cohort data came from Korea National Health Insurance claims for 124,606 primiparous women aged 25–40 and delivered in 2017. The 186 independent variables included demographic/socioeconomic determinants, disease information, and medication history. Machine learning analysis was used to establish the prediction model of PTB. Random forest variable importance was used for identifying major determinants of PTB and testing its associations with dental and gastrointestinal diseases, medication history, and socioeconomic status. Results: The random forest with oversampling data registered an accuracy of 84.03, and the areas under the receiver-operating-characteristic curves with the range of 84.03–84.04. Based on random forest variable importance with oversampling data, PTB has strong associations with socioeconomic status (0.284), age (0.214), year 2014 gastroesophageal reflux disease (GERD) (0.026), year 2015 GERD (0.026), year 2013 GERD (0.024), progesterone (0.024), year 2012 GERD (0.023), year 2011 GERD (0.021), tricyclic antidepressant (0.020) and year 2016 infertility (0.019). For example, the accuracy of the model will decrease by 28.4%, 2.6%, or 1.9% if the values of socioeconomic status, year 2014 GERD, or year 2016 infertility are randomly permutated (or shuffled). Conclusion: By using machine learning, we established a valid prediction model for PTB. PTB has strong associations with GERD and infertility. Pregnant women need close surveillance for gastrointestinal and obstetric risks at the same time.

Keywords: preterm birth; gastrointestinal disease; machine learning; population data (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
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