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    Artificial intelligence in prenatal diagnosis: Down syndrome risk assessment with the power of gradient boosting-based machine learning algorithms
    (2025) Yalçın, Emre; KOÇ, TARIK KAAN; ASLAN, SERPIL; Demir, Suleyman Cansun; evrüke, cüneyt; Sucu, Mete; AVAN, MESUT
    Objective: One of the most common chromosomal abnormalities seen during pregnancy is Down syndrome (Trisomy 21). To determine the risk of Down syndrome, first-trimester combined screening tests are essential. Using data from the first-trimester screening test, this study compares machine learning and deep learning models to forecast the risk of Down syndrome. Materials and Methods: Within the scope of the study, biochemical and biophysical data of 959 pregnant women who underwent first-trimester screening tests at Çukurova University Obstetrics and Gynecology Clinic between 2020-2024 were analyzed. After cleaning missing and erroneous data, various preprocessing and normalization techniques were applied to the final dataset consisting of 853 observations. Down syndrome risk prediction was performed using different machine learning models, and model performances were compared based on accuracy rates and other evaluation metrics. Results: Experimental results show that the CatBoost model provides the highest success rate, with an accuracy rate of 95.31%. In addition, the XGBoost and LightGBM models exhibited high performance, with accuracy rates of 95.19% and 94.84%, respectively. The study also examines the effects of the class imbalance problem on model performance in detail and evaluates various strategies to reduce this imbalance. Conclusion: The findings show that gradient boosting-based machine learning models have significant potential in Down syndrome risk prediction. This approach is expected to contribute to the reduction of unnecessary invasive tests and improve clinical decision-making processes by increasing the accuracy rate in prenatal screening processes. Future studies should aim to increase the generalization capacity of the model on larger data sets and to provide integration with different machine learning algorithms.
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    Explainable tabular deep learning models for antenatal cesarean delivery prediction in multiparous women
    (Bmc, 2026) Yalcin, Emre; Tanyildiz, Hayriye; Aslan, Serpil; Demir, Suleyman Cansun; Sucu, Mete; Uzay, Fatma Islek; Bicer, Ayse
    Background/Objectives Globalincreases in cesarean section (C-section) rates, often exceeding medical necessity, highlight the need for accurate antenatal prediction to support evidence-based birth planning. Reliable prediction of delivery mode is essential for reducing maternal and neonatal morbidity, improving clinical decision-making, and optimizing resource allocation. This study analyzes a publicly available dataset of 460 multiparous women, including 18 obstetric and antenatal variables, published by Yimer and Mekonnen. Methods Deep learning architectures were systematically evaluated for predicting delivery mode in multiparous pregnancies. Classical Multilayer Perceptrons (MLPs) served as baseline models, while modern tabular deep learning methods were assessed as advanced alternatives. Preprocessing included multiple imputation, outlier removal, and class balancing via SMOTE. Feature selection was performed using a hybrid Boruta-clinical expert strategy. Hyperparameters were tuned through Random Search. To improve interpretability, an explainability pipeline integrating SHAP and LIME was incorporated. Results Optimized MLPs produced modest performance gains, but dedicated tabular models demonstrated clear superiority. TabNet achieved the highest performance, with an ROC-AUC of 0.79 and a PR-AUC of 0.74, attributed to its attention and masking mechanisms and robust handling of minority classes. TabPFN and CBAM-MLP yielded stable and balanced results, whereas FT-Transformer showed competitive yet comparatively moderate accuracy. Conclusions The findings demonstrate that modern tabular deep learning approaches, particularly TabNet, surpass baseline MLP architectures in terms of accuracy, explainability, and clinical applicability for predicting C-section in multiparous women. This study presents the first comprehensive and explainable comparison of tabular deep learning models tailored to multiparous pregnancies, combining hybrid Boruta-expert feature selection with SHAP and LIME interpretability. TabNet emerges as the most promising candidate for integration into clinical decision support systems, contributing substantially to Al-driven strategies for addressing rising global C-section rates.
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    Hyperparameter optimization based machine learning approach for early diagnosis of fetal genetic disorders
    (Springer, 2025) Yalcin, Emre; Koc, Tarik Kaan; Aslan, Serpil; Demir, Suleyman Cansun; Aykut, Serdar; Sucu, Mete
    Prenatal screening is the process of analyzing various clinical variables to estimate the risk of genetic disorders like Down Syndrome (DS), which is distinguished by intellectual disability, distinct facial features, and developmental delays. The accuracy of these risk assessments is heavily reliant on the suitability of the risk algorithm for the target population. This study proposes an enhanced machine learning (ML) approach for predicting Down Syndrome (DS) risk using first-trimester screening (FTS) data. The dataset includes clinical information from 959 women with singleton pregnancies at the & Ccedil;ukurova University Gynecology and Obstetrics Unit between 2020 and 2024. To address limitations in existing studies, GPT-4 was utilized to generate synthetic minority-class samples, and advanced feature engineering techniques were incorporated to enhance model robustness and interpretability. A predictive ML model was created, and Hyperparameter Tuning (HT) was applied to optimize it for performance. Eight classifiers were tested, and CatBoost performed the best, achieving 97.39% accuracy and a 2.62% false-positive rate, outperforming the second-best classifier (XGBoost) across all primary evaluation metrics. These improvements highlight the novelty of the framework, particularly its integration of GPT-4-based augmentation and engineered biochemical interaction features. The results demonstrate the model's potential for reliable DS risk prediction, offering a more efficient and less invasive alternative to traditional diagnostic procedures. By enhancing early risk detection, the method could reduce unnecessary referrals for invasive tests like amniocentesis, thereby minimizing patient anxiety and potential complications. Overall, the study contributes to the development of intelligent, data-driven solutions for prenatal care.