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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

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Reducing False-Positive Results in Newborn Screening Using Machine Learning.

Gang Peng^1,2, Yishuo Tang¹, Tina M Cowan³

¹Department of Genetics, Yale University School of Medicine, New Haven, CT 06520, USA.

International Journal of Neonatal Screening

|March 20, 2020

Summary

This summary is machine-generated.

Newborn screening (NBS) machine learning reduces false positives for metabolic disorders. This tool improves diagnostic accuracy, ensuring fewer infants undergo unnecessary follow-up testing.

Keywords:

Random Forest false positive inborn metabolic disorders machine learning newborn screening second-tier testing tandem mass spectrometry

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Area of Science:

Biochemistry
Genetics
Public Health

Background:

Newborn screening (NBS) is crucial for early detection of inborn metabolic disorders.
NBS programs often yield false-positive results, necessitating further investigation.
Improving the accuracy of NBS interpretation is vital for public health.

Purpose of the Study:

To develop and evaluate a Random Forest machine learning classifier to enhance the prediction of true and false positives in NBS.
To reduce the number of false-positive results in NBS for specific metabolic disorders without compromising sensitivity.

Main Methods:

Trained a Random Forest classifier using 39 metabolic analytes and clinical data from 2777 screen-positive infants.
Evaluated classifier performance on data from the California NBS program for glutaric acidemia type 1 (GA-1), methylmalonic acidemia (MMA), ornithine transcarbamylase deficiency (OTCD), and very long-chain acyl-CoA dehydrogenase deficiency (VLCADD).

Main Results:

Random Forest analysis significantly reduced false positives: 89% for GA-1, 45% for MMA, 98% for OTCD, and 2% for VLCADD.
Key metabolic markers and previously identified analytes were ranked highly by the classifier.
The classifier demonstrated comparable performance to Clinical Laboratory Integrated Reports (CLIR) for GA-1 false positive classification.

Conclusions:

Random Forest machine learning effectively improves the accuracy of NBS by reducing false positives.
The developed tool offers a valuable method for interpreting complex NBS data, aiding clinical decision-making.
This approach enhances the efficiency and effectiveness of public health newborn screening programs.