Yazar "Aci, Cigdem Inan" seçeneğine göre listele

Listeleniyor 1 - 2 / 2
  • [ X ]
    Öğe
    A Feature Selection-Based Multi-Stage Methodology for Improving Driver Injury Severity Prediction on Imbalanced Crash Data
    (MDPI, 2025) Aci, Cigdem Inan; Mutlu, Gizen; Ozen, Murat; Sarac, Esra; Uzel, Vahide Nida Kilic
    Predicting driver injury severity is critical for enhancing road safety, but it is complicated because fatal accidents inherently create class imbalance within datasets. This study conducts a comparative analysis of machine-learning (ML) and deep-learning (DL) models for multi-class driver injury severity prediction using a comprehensive dataset of 107,195 traffic accidents from the Adana, Mersin, and Antalya provinces in Turkey (2018-2023). To address the significant imbalance between fatal, injury, and non-injury classes, the hybrid SMOTE-ENN algorithm was employed for data balancing. Subsequently, feature selection techniques, including Relief-F, Extra Trees, and Recursive Feature Elimination (RFE), were utilized to identify the most influential predictors. Various ML models (K-Nearest Neighbors (KNN), XGBoost, Random Forest) and DL architectures (Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN)) were developed and rigorously evaluated. The findings demonstrate that traditional ML models, particularly KNN (0.95 accuracy, 0.95 F1-macro) and XGBoost (0.92 accuracy, 0.92 F1-macro), significantly outperformed DL models. The SMOTE-ENN technique proved effective in managing class imbalance, and RFE identified a critical 25-feature subset including driver fault, speed limit, and road conditions. This research highlights the efficacy of well-preprocessed ML approaches for tabular crash data, offering valuable insights for developing robust predictive tools to improve traffic safety outcomes.
  • [ X ]
    Öğe
    Deep Learning-Based Prediction Models for theDetection of Vitamin D Deficiency and25-Hydroxyvitamin D Levels Using Complete BloodCount Tests
    (Editura Acad Romane, 2024) Essiz, Ugur Engin; Aci, Cigdem Inan; Sarac, Esra; Aci, Mehmet
    Vitamin D (VitD) is an essential nutrient that is critical for the well-being of both adults and children, and its deficiency is recognized as a precursor to several diseases. In previous studies, researchers have approached the problem of detecting vitamin D deficiency (VDD) as a single sufficient/deficient classification problem using machine learning or statistics-based methods. The main objective of this paper is to predict a patient's VitD status (i.e., sufficiency, insufficiency, or deficiency), severity of VDD (i.e., mild, moderate, or severe), and 25-hydroxyvitamin D (25(OH)D) level in a separate deep learning (DL)-based models. An original dataset consisting of complete blood count (CBC) tests from 907 patients, including 25(OH)D concentrations, collected from a public health laboratory was used for this purpose. CNN, RNN, LSTM, GRU and Auto-encoder algorithms were used to develop DL-based models. The top 25 features in the CBC tests were carefully selected by implementing the Extra Trees Classifier and Multi-task LASSO feature selection algorithms. The performance of the models was evaluated using metrics such as accuracy, F1-score, mean absolute error, root mean square error and R-squared. Remarkably, all three models showed satisfactory results when compared to the existing literature; however, the CNN-based prediction models proved to be the most successful.