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Enhancing TSH-based congenital hypothyroidism screening using machine learning and resampling algorithms
Alexander De Furia1,2, Paula Branco3, Matthew Henderson4,5
1School of Electrical Engineering and Computer Science, University of Ottawa, 800 King Edward Ave., Ottawa, Ontario, K1N 5N6, Canada. adefu020@uottawa.ca.
Insights
Machine learning significantly improves congenital hypothyroidism screening by boosting positive predictive value 60% while maintaining 100% sensitivity. This advanced approach reduces false positives and unnecessary diagnostic costs for newborns.
Area of Science:
- Medical screening
- Machine learning applications
- Neonatal health
Background:
- Congenital hypothyroidism (CH) is a leading cause of preventable intellectual disability.
- Current newborn screening for CH relies on thyroid-stimulating hormone (TSH), facing challenges with low positive predictive value (PPV).
- Previous machine learning attempts for CH screening were hindered by data imbalance and limited predictive features.
Purpose of the Study:
- To conduct a comprehensive evaluation of machine learning algorithms for congenital hypothyroidism screening.
- To address the limitations of current TSH-based screening methods, specifically low PPV.
- To develop a more accurate and efficient screening model for CH.
Main Methods:
- Analysis of data from 616,910 infants screened between 2019 and 2024.
- Training and evaluation of 576 distinct machine learning models using 12 classification and 12 resampling algorithms.
- Optimization for sensitivity and PPV using stratified 5-fold cross-validation and assessment of model explainability via SHAP values.
Main Results:
- A RUSBoost classifier with Gaussian Noise resampling achieved 100% sensitivity and 16.8% PPV.
- This represents a 60% improvement in PPV compared to current screening approaches.
- TSH remained the primary predictor, but the model incorporated additional features to enhance performance.
Conclusions:
- Machine learning models demonstrated no missed cases of CH and significantly improved screening performance.
- These algorithms offer a promising alternative to refine TSH-based CH screening.
- The findings suggest potential for reducing false positives, stress, and costs in global newborn screening programs.
Purpose:
Congenital hypothyroidism (CH) is a common cause of severe intellectual disability, affecting approximately 1 in 2,000 newborns globally. Treatable with early intervention, congenital hypothyroidism has long been a target of newborn screening programs. Current thyroid stimulating hormone (TSH) based programs suffer from low positive predictive value, resulting in unnecessary diagnostic investigations. Congenital hypothyroidism screening has proven challenging for machine learning previously due to massive class imbalance and having a single well known predictor, preventing acceptable screening sensitivity. This study represents the most comprehensive evaluation of machine learning for congenital hypothyroidism screening to date.
Methods:
Analyzing data from 616,910 infants screened by Newborn Screening Ontario between 2019 and 2024. 12 classification and 12 resampling algorithms were trained using 4 different optimization metrics, for a total of 576 distinct models evaluated using stratified 5-fold cross-validation to ensure robustness. Models were optimized for sensitivity and then positive predictive value using various metrics. Model explainability was assessed using SHAP values and feature importances.
Results:
We were able to create a model achieving 16.8% PPV while maintaining 100% sensitivity using a RUSBoost classifier and Gaussian Noise resampling. This represents a 60% improvement in positive predictive value over the current approach. TSH remained the dominant predictor as in current screening, but our model was able to include minor amounts of additional information from other features to improve performance.
Conclusion:
These machine learning algorithms show no missed cases of CH and are able to significantly improve performance across robust testing. The findings suggest that machine learning offers a promising avenue for refining TSH-based CH screening processes, reducing false positives, and alleviating unnecessary stress and costs associated with current methods used by the majority of newborn screening programs globally.
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