Comparison of Machine Learning Algorithms to Predict Down Syndrome During the Screening of the First Trimester of Pregnancy  
[PERSON]  
1 Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), 2009 Donostia-San Sebastian, Spain; [EMAIL_ADDRESS]  
[PERSON]  
1 Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), 2009 Donostia-San Sebastian, Spain; [EMAIL_ADDRESS]  
[PERSON]  
2 Departament of Computer Architecture and Technology, University of the Basque Country (UPV/EHU), 20018 Donostia-San Sebastian, Spain; [EMAIL_ADDRESS]  
[PERSON]  
2 Departament of Computer Architecture and Technology, University of the Basque Country (UPV/EHU), 20018 Donostia-San Sebastian, Spain; [EMAIL_ADDRESS]  
Footnote 1: email: [EMAIL_ADDRESS]  
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Footnote 6: email: jg  
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Notably, the linear models (LR and SGD) assign greater importance to PAPP-A and B-HCG compared to the other models, for which NT is the most important variable. +++  
Table 3 shows the sensitivity and specificity of each model using two different thresholds. The first is the default threshold of 0.5, where any new individual prediction with a probability greater than 0.5 is classified as positive. The second method, called gmeans, seeks the optimal threshold for a ROC curve by balancing sensitivity and specificity, which is particularly useful for unbalanced classification problems, as is the case here. In a clinical setting, even a seemingly small increase in sensitivity can result in a meaningful reduction in false negatives, ensuring that more affected pregnancies are correctly identified. Similarly, a slight improvement in specificity reduces false positives, minimizing unnecessary anxiety and invasive procedures for low-risk pregnancies. It is also important to acknowledge the inherent trade-off between sensitivity and specificity: models like XGBoost with the gmeans threshold prioritize sensitivity to maximize the detection of positive cases, potentially leading to a slight decrease in specificity; conversely, other models may exhibit higher specificity at the cost of slightly lower sensitivity. The optimal model and threshold depend on the specific clinical priorities and the relative costs of false positives and false negatives. Finally, Figure 8 presents a confusion matrix with the test data partition of the XGBoost model with the threshold selected by the gmeans method (0.41). It is clear how practically all of the positive cases are classified correctly (0.943 sensitivity), even with the large unbalance problem in the initial dataset. This is largely due to the data balancing techniques used during training. In the case of the negatives, it can be seen to have an equally high predictivity (0.939 specificity). \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **Threshold** & **ROC-AUC** & **PR-AUC** & **Sensitivity** & **Specificity** \\ \hline LR & default & 0.981 & 0.3468 & 0.934 & 0.937 \\  & gmeans & & & 0.934 & 0.946 \\ \hline SGD & default & 0.979 & 0.5287 & 0.934 & 0.944 \\  & gmeans & & & 0.962 & 0.906 \\ \hline MLP & default & 0.976 & 0.5123 & 0.943 & 0.938 \\  & gmeans & & & 0.962 & 0.929 \\ \hline ET & default & 0.975 & 0.5018 & 0.934 & 0.945 \\  & gmeans & & & 0.934 & 0.936 \\ \hline GB & default & 0.983 & 0.6270 & 0.934 & 0.943 \\  & gmeans & & & 0.925 & 0.949 \\ \hline RF & default & 0.979 & 0.5637 & 0.934 & 0.938 \\  & gmeans & & & 0.934 & 0.938 \\ \hline SVM & default & 0.979 & 0.2822 & 0.915 & 0.966 \\  & gmeans & & & 0.934 & 0.941 \\ \hline KNN & default & 0.982 & 0.5118 & 0.877 & 0.965 \\  & gmeans & & & 0.953 & 0.925 \\ \hline XGB & default & 0.982 & 0.6272 & 0.915 & 0.948 \\  & gmeans & & & 0.943 & 0.939 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Model performance comparison with default and gmeans thresholds.