Korea University College of Medicine Reports Findings in Heart Disease (Preterm birth and maternal heart disease: A machine learning analysis using the Korean national health insurance database): Heart Disorders and Diseases – Heart Disease

2023 APR 17 (NewsRx) -- By a News Reporter-Staff News Editor at Information Technology Business Daily -- New research on Heart Disorders and Diseases - Heart Disease is the subject of a report. According to news originating from Seoul, South Korea, by NewsRx correspondents, research stated, “Maternal heart disease is suspected to affect preterm birth (PTB); however, validated studies on the association between maternal heart disease and PTB are still limited. This study aimed to build a prediction model for PTB using machine learning analysis and nationwide population data, and to investigate the association between various maternal heart diseases and PTB.”

Our news journalists obtained a quote from the research from the Korea University College of Medicine, “A population-based, retrospective cohort study was conducted using data obtained from the Korea National Health Insurance claims database, that included 174,926 primiparous women aged 25-40 years who delivered in 2017. The random forest variable importance was used to identify the major determinants of PTB and test its associations with maternal heart diseases, i.e., arrhythmia, ischemic heart disease (IHD), cardiomyopathy, congestive heart failure, and congenital heart disease first diagnosed before or during pregnancy. Among the study population, 12,701 women had PTB, and 12,234 women had at least one heart disease. The areas under the receiver-operating-characteristic curves of the random forest with oversampling data were within 88.53 to 95.31. The accuracy range was 89.59 to 95.22. The most critical variables for PTB were socioeconomic status and age. The random forest variable importance indicated the strong associations of PTB with arrhythmia and IHD among the maternal heart diseases. Within the arrhythmia group, atrial fibrillation/flutter was the most significant risk factor for PTB based on the Shapley additive explanation value. Careful evaluation and management of maternal heart disease during pregnancy would help reduce PTB.”

According to the news editors, the research concluded: “Machine learning is an effective prediction model for PTB and the major predictors of PTB included maternal heart disease such as arrhythmia and IHD.”

For more information on this research see: Preterm birth and maternal heart disease: A machine learning analysis using the Korean national health insurance database. PLOS ONE, 2023;18(3). PLOS ONE can be contacted at: Public Library Science, 1160 Battery Street, Ste 100, San Francisco, CA 94111, USA. (Public Library of Science - www.plos.org; PLOS ONE - www.plosone.org)

The news correspondents report that additional information may be obtained from Eun-Saem Choi, Dept. of Obstetrics and Gynecology, Korea University College of Medicine, Korea University Anam Hospital, Seoul, South Korea. Additional authors for this research include Jue Seong Lee, Yujin Hwang, Kwang-Sig Lee and Ki Hoon Ahn.

The publisher’s contact information for the journal PLOS ONE is: Public Library Science, 1160 Battery Street, Ste 100, San Francisco, CA 94111, USA.

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