Evaluation of A Novel Risk Factor Screening Tool for Gestational Diabetes Mellitus: A Machine Learning Based Predictive Method
Bibliographic record
Abstract
Introduction: Gestational diabetes mellitus (GDM) is a significant pregnancy complication linked to adverse outcomes for both mother and child. Early identification of high-risk individuals is crucial for effective management and prevention for the onset/progression of the GDM. Our study aims to a) evaluate the effectiveness of a newly developed machine learning based risk factor screening tool for predicting GDM and b) to compare its predictive performance against established models and current literature. Methods: This study explored SNP data from the leptin (LEP) and leptin receptor (LEPR) genes to develop machine learning models for predicting gestational diabetes mellitus (GDM). It included data preprocessing, such as cleaning and feature selection, focusing on genetic markers, metabolic parameters, and demographic information. Various algorithms, including Logistic Regression, Decision Trees, and Random Forests, were used, and their performance was evaluated using metrics like accuracy and ROC-AUC to determine the best model for GDM prediction. Results: The newly developed screening tool demonstrated a sensitivity of 85%, specificity of 78%, positive predictive value (PPV) of 68%, and negative predictive value (NPV) of 90% in predicting GDM. Comparatively, machine learning models showed higher sensitivity (90-95%) but lower specificity (65-75%). Conclusion: The developed risk factor screening tool is a viable method for predicting GDM, with accuracy metrics comparable to advanced machine learning models and established literature. Future research should focus on refining these tools and exploring their integration into routine prenatal care to enhance early detection and intervention strategies for GDM.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".