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Related Experiment Video

Updated: Jan 13, 2026

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

Predictive analysis of birth intention using machine learning.

Hyun Kyoung Kim¹

¹Department of Nursing, Kongju National University, Gongju, South Korea.

Journal of Investigative Medicine : the Official Publication of the American Federation for Clinical Research

|January 9, 2026

Summary

This study used machine learning to predict birth intention, finding that maternal stress, relationship satisfaction, and partner conflict are key factors. Understanding these predictors can help support families.

Keywords:

birth machine learning mothers pregnancy psychological stress

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Published on: October 10, 2018

Area of Science:

Reproductive Health
Machine Learning in Healthcare
Sociology of Family

Background:

Birth intention is a critical factor in reproductive decision-making.
Identifying predictors of birth intention is essential for effective family planning and support.
Previous research has explored various demographic and psychosocial factors, but predictive modeling remains an active area.

Purpose of the Study:

To identify key determinants of birth intention.
To develop and evaluate a machine learning model for predicting future birth intention.
To classify individuals at risk for specific birth intentions.

Main Methods:

Analysis of data from 2,580 subjects using the Korean Early Childhood Education & Care Panel (K-ECEC-P).
Application of machine learning techniques including decision tree, random forest classifier, and logistic regression.
Model performance evaluated using precision, accuracy, recall, F1-score, and area under the curve (AUC).

Main Results:

The Random Forest model achieved an AUC of 82%, demonstrating strong predictive performance.
Key predictors identified include marriage period, age, stress, prenatal weight, number of children, maternal rearing behavior, conflict with partner, marriage satisfaction, depression, and type of birth.
Maternal psychological status (stress, depression) and relational factors (conflict, satisfaction) were significant predictors.

Conclusions:

Machine learning models can effectively predict birth intention.
Maternal psychological well-being and relationship dynamics are crucial determinants of birth intention.
Family-centered care and social support addressing these factors are vital during the postpartum period.