Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Infertility in Females01:28

Infertility in Females

3.7K
Female infertility is defined as the inability to conceive after a year of regular, unprotected intercourse and affects about 10–15% of couples worldwide. The primary cause of female infertility is ovulatory disorders, which hinder the release of eggs. These disorders can be classified as hypothalamic amenorrhea, polycystic ovarian syndrome (PCOS), premature ovarian failure, and hyperprolactinemic anovulation disorders.
Endometriosis, a condition characterized by abnormal growth of...
3.7K
Regression Toward the Mean01:52

Regression Toward the Mean

6.9K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.9K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Advanced maternal age and infertility: The role of biological aging, lifestyle, and biomarkers.

Journal of reproductive immunology·2026
Same author

The association between dairy cattle ownership and nutritional status of children under five in rural Bangladesh: a cross-sectional study.

Frontiers in nutrition·2026
Same author

Precision Biomarker Identification in Gynecological Cancers Using Coexpression Networks and Attention-Based LSTM in Healthcare 4.0.

Diagnostics (Basel, Switzerland)·2026
Same author

Rice price volatility and dietary diversity in Bangladeshi farm households: panel data evidence.

Frontiers in nutrition·2026
Same author

Blinder-Oaxaca decomposition analysis of the urban-rural gap in child nutrition in Bangladesh.

Scientific reports·2025
Same author

Factors affecting women's nutritional security in rural Bangladesh: The role of livestock and other socioeconomic characteristics.

PloS one·2025

Related Experiment Video

Updated: Jan 18, 2026

A Neural Network-Based Identification of Developmentally Competent or Incompetent Mouse Fully-Grown Oocytes
10:04

A Neural Network-Based Identification of Developmentally Competent or Incompetent Mouse Fully-Grown Oocytes

Published on: March 3, 2018

7.1K

Temporal Trends and Machine Learning-Based Risk Prediction of Female Infertility: A Cross-Cohort Analysis Using

Ismat Ara Begum1, Deepak Ghimire2, A S M Sanwar Hosen3

  • 1Department of Biomedical Sciences and Institute for Medical Science, Jeonbuk National University Medical School, Jeonju 54907, Republic of Korea.

Diagnostics (Basel, Switzerland)
|September 13, 2025
PubMed
Summary

Female infertility prevalence rose significantly, potentially due to post-pandemic effects. Machine learning models accurately predicted infertility risk using key clinical factors, aiding future reproductive health strategies.

Keywords:
NHANESROC curvefemale infertilitylogistic regressionmachine learningreproductive healthrisk prediction

More Related Videos

A Novel Use of Three-dimensional High-frequency Ultrasonography for Early Pregnancy Characterization in the Mouse
07:04

A Novel Use of Three-dimensional High-frequency Ultrasonography for Early Pregnancy Characterization in the Mouse

Published on: October 24, 2017

8.8K
Using Mouse Oocytes to Assess Human Gene Function During Meiosis I
11:13

Using Mouse Oocytes to Assess Human Gene Function During Meiosis I

Published on: April 10, 2018

9.4K

Related Experiment Videos

Last Updated: Jan 18, 2026

A Neural Network-Based Identification of Developmentally Competent or Incompetent Mouse Fully-Grown Oocytes
10:04

A Neural Network-Based Identification of Developmentally Competent or Incompetent Mouse Fully-Grown Oocytes

Published on: March 3, 2018

7.1K
A Novel Use of Three-dimensional High-frequency Ultrasonography for Early Pregnancy Characterization in the Mouse
07:04

A Novel Use of Three-dimensional High-frequency Ultrasonography for Early Pregnancy Characterization in the Mouse

Published on: October 24, 2017

8.8K
Using Mouse Oocytes to Assess Human Gene Function During Meiosis I
11:13

Using Mouse Oocytes to Assess Human Gene Function During Meiosis I

Published on: April 10, 2018

9.4K

Area of Science:

  • Reproductive Health and Epidemiology
  • Biostatistics and Machine Learning

Background:

  • Female infertility is a major global health issue with under-examined trends and risk factors.
  • Nationally representative data on infertility prevalence and prediction models are limited.

Purpose of the Study:

  • To investigate temporal trends in female infertility prevalence.
  • To evaluate Machine Learning (ML) models for predicting infertility risk using clinical features.

Main Methods:

  • Analysis of US women aged 19-45 from NHANES cycles (2015-2023) with complete infertility-related data.
  • Comparison of infertility prevalence using ANOVA and Chi-square tests.
  • Development and validation of six ML models (LR, RF, XGBoost, NB, SVM, Stacking) using GridSearchCV and cross-validation.

Main Results:

  • Infertility prevalence increased from 14.8% (2017-2018) to 27.8% (2021-2023).
  • Prior childbirth was protective (Adjusted OR ≈0.00); menstrual irregularity was associated with infertility (OR=0.55).
  • ML models achieved high predictive performance (AUC >0.96) with a minimal feature set.

Conclusions:

  • Rising infertility prevalence in US women highlights urgent public health concerns.
  • Interpretable and ensemble ML models effectively predict infertility risk, supporting surveillance and personalized care.
  • Future research should incorporate broader sociodemographic and behavioral factors for enhanced precision.