Predicting lifetime cervical cancer screening among sexually active women in low- and middle-income…

AI / informaticsAJOG global reports· Aug 2026

Predicting lifetime cervical cancer screening among sexually active women in low- and middle-income countries using machine learning models: evidence from Demographic and Health Surveys.

Fentahun Bikale Kebede, Kusse Urmale Mare, Fetlework Gubena Arage … Bewuketu Terefe

Gynecologic CancerAI / informaticsQ1IF 2.6

Random forest model predicted cervical cancer screening history with 80% accuracy

Predicting lifetime cervical cancer screening among sexually active women in low- and middle-income countries using machine learning models: evidence from Demographic and Health Surveys.

Fentahun Bikale Kebede … Bewuketu Terefe

AJOG global reports · Aug 2026 · Q1 in Obstetrics & Gynecology

Background

Cervical cancer poses a major health risk in low- and middle-income countries (low- and middle-income countries), with high morbidity and mortality among women.

Methods

Using recent Demographic and Health Surveys (Demographic and Health Surveys) data from 19 low- and middle-income countries, this study estimated lifetime cervical cancer screening among women aged 15 to 49 years.

n = 261,371 women

Results

the model correctly identified screening history 4 times out of 5

Random Forest

80%

Extra Trees

79%

More results

Key predictors included household size, wealth, women's age, and education.

Ensemble methods such as random forests and extra trees demonstrated strong generalization, making them useful for targeted interventions.

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Conclusion · 1 of 2

Ensemble algorithms such as random forests and extra trees predict lifetime cervical cancer screening with ∼80% accuracy in LMICS. The study emphasizes targeted awareness, education, and age-sensitive screening.

Conclusion · 2 of 2

Findings will be tested in other populations to support clinical decision systems.

Predicting lifetime cervical cancer screening among sexually active women in low- and middle-income countries using machine learning models: evidence from Demographic and Health Surveys.

Predicting lifetime cervical cancer screening among sexually active women in low- and middle-income countries using machine learning models: evidence from Demographic and Health Surveys.

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