Yazar "Yalcin, Emre" seçeneğine göre listele

Listeleniyor 1 - 4 / 4
  • Küçük Resim Yok
    Öğe
    A Hybrid Artificial Intelligence Approach for Down Syndrome Risk Prediction in First Trimester Screening
    (Mdpi, 2025) Yalcin, Emre; Aslan, Serpil; Togacar, Mesut; Demir, Suleyman Cansun
    Background/Objectives: The aim of this study is to develop a hybrid artificial intelligence (AI) approach to improve the accuracy, efficiency, and reliability of Down Syndrome (DS) risk prediction during first trimester prenatal screening. The proposed method transforms one-dimensional (1D) patient data-including features such as nuchal translucency (NT), human chorionic gonadotropin (hCG), and pregnancy-associated plasma protein A (PAPP-A)-into two-dimensional (2D) Aztec barcode images, enabling advanced feature extraction using transformer-based deep learning models. Methods: The dataset consists of 958 anonymous patient records. Each record includes four first trimester screening markers, hCG, PAPP-A, and NT, expressed as multiples of the median. The DS risk outcome was categorized into three classes: high, medium, and low. Three transformer architectures-DeiT3, MaxViT, and Swin-are employed to extract high-level features from the generated barcodes. The extracted features are combined into a unified set, and dimensionality reduction is performed using two feature selection techniques: minimum Redundancy Maximum Relevance (mRMR) and RelieF. Intersecting features from both selectors are retained to form a compact and informative feature subset. The final features are classified using machine learning algorithms, including Bagged Trees and Naive Bayes. Results: The proposed approach achieved up to 100% classification accuracy using the Naive Bayes classifier with 1250 features selected by RelieF and 527 intersecting features from mRMR. By selecting a smaller but more informative subset of features, the system significantly reduced hardware and processing demands while maintaining strong predictive performance. Conclusions: The results suggest that the proposed hybrid AI method offers a promising and resource-efficient solution for DS risk assessment in first trimester screening. However, further comparative studies are recommended to validate its performance in broader clinical contexts.
  • Küçük Resim Yok
    Öğe
    A Methodological Study on Fetal Health Classification Using Optimized LightGBM with SMOTE and Optuna-Based Hyperparameter Optimization
    (Turkiye Klinikleri, 2025) Yalcin, Emre; Aslan, Serpil; Tanyildiz, Hayriye
    Objective: Fetal health classification is of great clinical importance as it allows the early detection and management of fetal health problems during pregnancy.This study aims to enhance fetal health classification by integrating Light Gradient Boosting Machine (LightGBM), synthetic minority oversampling technique (SMOTE), and Optuna-based hyperparameter tuning. The goal is to improve classification accuracy, address class imbalance, and optimize model performance in predicting normal, suspicious, and pathological fetal health conditions. Material and Methods: The study utilized the University of California, Irvine Cardiotocography Data Set which contains 2.126 fetal cardiotocography (CTG) records classified into 3 categories. To handle class imbalance, SMOTE is applied. Various machine learning models [Stochastic Gradient Descent Classifier, Gradient Boosting, Decision Tree, Support Vector Machine, K-Nearest Neighbors, and Logistic Regression (LG)] were compared, with LightGBM selected due to its efficiency in structured medical data processing. Optuna is used for hyperparameter tuning. The dataset was split into 80% training and 20% testing, and model performance was assessed using accuracy, precision, recall, and F1-score. Results: The optimized LightGBM+SMOTE+Optuna model achieved 99% accuracy, significantly outperforming the other classifiers. The recall and F1-score for the suspicious and pathological classes improved, reducing the misclassification rates. The confusion matrix confirmed a substantial decrease in errors, demonstrating the model's robustness and reliability. Conclusion: The proposed model successfully enhances fetal health classification accuracy by addressing class imbalance and optimizing hyperparameters. The integration of LightGBM, SMOTE, and Optuna improves model generalization, making it a valuable tool for fetal health assessment. Future studies could explore deep learning approaches to further refine classification performance and support clinical decision-making.
  • Küçük Resim Yok
    Öğe
    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.
  • Küçük Resim Yok
    Öğe
    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.