Evaluation of Synthetic Data Generating Methods in Down Syndrome Prediction During the First Trimester Screening in Vietnam

Down’s Syndrome (DS) is one of the most prevalent types of chromosomal abnormality. Developmental delays and disabilities on the physical and mental levels result from this syndrome. Hence, it must be detected as soon as possible. However, Down syndrome screening data tend to have a large overall data pool with a small proportion of positive cases, leading to an imbalanced class problem that causes classifiers to become biased. Moreover, the number of these data is also another problem, especially in Vietnam. These data are often private and challenging to collect correctly. This study utilizes synthetic data generating methods to maximize the detection rate of some traditional classification models. The final results of this study indicate that it is possible to improve the down syndrome prediction quality of the classifiers by adequately incorporating the SMOTE-based and GANs-based methods to generate synthetic data.