Application of intelligent algorithms in Down syndrome screening during second trimester pregnancy

ARTICLE HIGHLIGHTS

Research background

Down syndrome (DS) is one of the most common chromosomal aneuploidy diseases. Due to the limitations of DS screening technology, approximately 30% of DS cases could not be found. Therefore, the detection rate and false positive rate of these methods need to be improved.

Research motivation

Issues in recent years have included how to fully utilize clinical cumulative data to provide consultation reference and rational basis for patients and construction of statistical models for DS screening that are suitable for specific regions.

Research objectives

This study aimed to use intelligent algorithms in machine learning for modeling and analysis of prenatal DS screening.

Research methods

This was a retrospective study of a clinical prenatal screening dataset. We designed and developed intelligent algorithms based on the synthetic minority over-sampling technique(SMOTE)-Tomek and adaptive synthetic sampling over-sampling techniques. The machine learning model was established and used for DS screening evaluation.

Research results

The dataset showed a large difference between the numbers of DS affected and non-affected cases. A combination of over-sampling and under-sampling techniques can greatly increase the performance of the algorithm at processing non-balanced datasets. As the number of iterations increases, the combination of the classification and regression tree algorithm and the SMOTE-Tomek over-sampling technique can obtain a high detection rate (DR) while keeping the false positive rate(FPR) to a minimum.

Research conclusions

Intelligent algorithms achieved good results on the DS screening dataset. When the T21 risk cutoff value was set to 270, machine learning methods had a higher DR and a lower FPR than statistical methods.

Research perspectives

The findings of this study suggest that the establishment and application of machine learning models will help to improve the detection rate of DS.