ArticleMedicine2025
Machine learning models for the prediction of uterine fibroids.
Article in Medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this cross-sectional study, we developed and validated a predictive model for uterine fibroid risk using routine physical examination indicators and 5 machine learning algorithms: logistic regression, random forest, k-nearest neighbors, categorical boosting (CatBoost), and light gradient boosting machine. The primary dataset consisted of health examination records from the MJ Health Screening Center in Beijing, China (2013-2023), while an independent external validation dataset (2024) was used to assess generalizability. LASSO regression identified 13 significant predictors, including age, body mass index, total cholesterol, diastolic blood pressure, and marital status. Among the models, CatBoost demonstrated the best performance, achieving an area under the curve of 0.808 in the internal validation dataset and 0.821 in the external validation dataset, indicating strong predictive capability and robustness. SHapley additive exPlanations analysis revealed that age and body mass index were the most critical predictors, and that total cholesterol was a key predictive feature; its implications for lipid metabolism are further discussed in the main text. Despite its strengths in area under the curve, specificity, and sensitivity, the model exhibited limitations in precision (0.475) and moderate accuracy (0.742), indicating challenges in controlling false-positive rates. The results indicate that the model is a potentially effective screening tool for identifying high-risk individuals who may benefit from further diagnostic evaluation. While this study validates the feasibility of using routine health examination data combined with the CatBoost algorithm for early risk assessment of uterine fibroids, it also highlights the need for cautious interpretation of the model's predictions in clinical practice. Future research should focus on multicenter, large-scale studies to enhance the model's generalizability and incorporate additional predictive factors to optimize performance.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.