A Combination of Multi-Branch CNN and Feature Rearrangement for Down Syndrome Prediction

One of the most common congenital anomalies in fetuses is known to be Down syndrome (DS). DS causes various adverse effects on the quality and length of life of children having DS and their families. Therefore, prenatal screening and diagnosis for DS are essential and valuable in antenatal care. Recently, machine learning methods for DS detection have become widespread. However, the existing methods, which use the traditional machine learning models, usually have several limitations while facing imbalanced data and missing data. This paper proposes a multi-branch CNN model combined with a feature rearrangement approach to improve the quality of DS prediction from prenatal screening data. The proposed feature rearrangement approach utilizes Pearson correlation testing and feature grouping to create a proper arrangement for the CNN model. Despite the imbalanced and highly missing data, the experiments show promising results with a Recall of 0.9023, F1-score of 0.8969, and balanced accuracy of 0.9314. These achievements outperform several traditional machine learning and attention-based deep learning models.