Application of machine learning to predict delayed fecundability among women in sub-Saharan Africa

Many women struggle to get pregnant even after trying for a year or more, a condition called delayed fecundability. This issue is becoming more common worldwide and can signal problems with fertility. We used data from surveys in five African countries to find out which factors may predict this delay. Using computer models that can learn from data, we found that age, marital status, and past use of fertility treatments were strong predictors. Our best model correctly identified nearly 80% of women with delayed fecundability. To make the findings easy to understand, we used a method that explains how each factor influences the result. We also found that the effects of these factors vary by age and treatment history. Our results can help health workers identify women at higher risk earlier, especially in places where fertility services are limited, and provide them with better, more personalized care.

Authors

Institutions

Publication Details

Journal
Reproduction and Fertility
Published
2025-09-16
DOI
https://doi.org/10.1530/raf-25-0068
Primary Topic
Insurance, Mortality, Demography, Risk Management
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Application of machine learning to predict delayed fecundability among women in sub-Saharan Africa

Banchlay Addis, Meron Asmamaw Alemayehu, Habtamu Abebe, Gebrie Getu Alemu et al.
Reproduction and Fertility
Insurance, Mortality, Demography, Risk Management
article

Application of machine learning to predict delayed fecundability among women in sub-Saharan Africa

Banchlay Addis, Meron Asmamaw Alemayehu, Habtamu Abebe, Gebrie Getu Alemu, Habtamu Wagnew Abuhay, Tilahun Yemanu Birhan, Eyob Akalewold Alemu, Nebiyu Mekonnen Derseh, Getaneh Awoke Yismaw, Emebet Birhanu Lealem, Mekuriaw Nibret Aweke, Berhanie Addis Ayele, Fetlework Gubena Arage, Tigist Kifle Tsegaw
article en

Abstract

Many women struggle to get pregnant even after trying for a year or more, a condition called delayed fecundability. This issue is becoming more common worldwide and can signal problems with fertility. We used data from surveys in five African countries to find out which factors may predict this delay. Using computer models that can learn from data, we found that age, marital status, and past use of fertility treatments were strong predictors. Our best model correctly identified nearly 80% of women with delayed fecundability. To make the findings easy to understand, we used a method that explains how each factor influences the result. We also found that the effects of these factors vary by age and treatment history. Our results can help health workers identify women at higher risk earlier, especially in places where fertility services are limited, and provide them with better, more personalized care.

Reproduction and FertilityVol. 6(4)
University of Gondar (ET)
Openalex Percentile: Top 56%
Insurance, Mortality, Demography, Risk Management
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Application of machine learning to predict delayed fecundability among women in sub-Saharan Africa — Banchlay Addis, Meron Asmamaw Alemayehu, et al. · Reproduction and Fertility (2025) | TGRS Research Map | TGRS