The Adaptive Manifold Concept with Regularized Autoencoders (AMRAE) algorithm is introduced as a novel dimensionality reduction technique that integrates manifold learning with autoencoders to capture the intrinsic geometry of high-dimensional data effectively. The study evaluates the impact of various adjustments and enhancements within the "Manifold Adjustment Box," including different regularization techniques, activation functions, and architectural choices, across diverse datasets. Key findings demonstrate that configurations such as Leaky ReLU Activation and Batch Norm Layer consistently improve accuracy, results highlight the flexibility and robustness of AMRAE. The results underscore... More >

This study developed a machine learning model for early risk stratification of Down syndrome by integrating maternal serum biomarkers and ultrasound measurements. A retrospective multicentre dataset was used, including maternal age, AFP, HCG, INHIBIN-A, and ultrasound parameters (NT, CRL). After imputing missing data and engineering features (e.g., Age_NT_interaction), a Gradient Boosting Machine (GBM) was trained and evaluated using AUROC, precision, recall, and F1-score. The model achieved high performance (AUROC: 0.9921; precision: 1.00; F1-score: 0.91; accuracy: 0.97). SHAP analysis identified key interactions—particularly Age_NT, Age_HCG, and Age_PAPP-A—as major contributors. High m... More >