Artificial Neural Network Analysis of Spontaneous Preterm Labor and Birth and Its Major Determinants

Kwang-Sig LEE; Ki-Hoon AHN

Kwang-Sig LEE; Ki-Hoon AHN.

Journal of Korean Medical Science ; : e128-2019.

Article in English | WPRIM (Western Pacific) | ID: wpr-764962

Responsible library: WPRO

ABSTRACT

ABSTRACT

BACKGROUND:

Little research based on the artificial neural network (ANN) is done on preterm birth (spontaneous preterm labor and birth) and its major determinants. This study uses an ANN for analyzing preterm birth and its major determinants.

Subject(s)

Female; Humans; Pregnancy; Adenomyosis; Biopsy; Body Mass Index; Conization; Diabetes Mellitus; Forests; Hypertension; Korea; Logistic Models; Machine Learning; Mass Screening; Myoma; Obstetric Labor, Premature; Parity; Parturition; Placenta Previa; Premature Birth; Seoul

Preterm Birth; Hypertension; Diabetes Mellitus; Prior Conization; Cervical-Length Screening

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Full text: Available Health context: SDG3 - Health and Well-Being Health problem: Target 3.4: Reduce premature mortality due to noncommunicable diseases Database: WPRIM (Western Pacific) Main subject: Parity / Placenta Previa / Biopsy / Body Mass Index / Logistic Models / Forests / Mass Screening / Conization / Parturition / Premature Birth Type of study: Risk factors / Screening study Limits: Female / Humans / Pregnancy Country/Region as subject: Asia Language: English Journal: Journal of Korean Medical Science Year: 2019 Document type: Article

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