Ermioni Prapa
Ermioni Prapa has a total of 11 co-authors. Here are the top co-authors by number of shared publications:
- Agni Pantou
1st Department of Obstetrics and Gynecology, ‘Alexandra’ General Hospital, National and Kapodistrian University of Athens, 80 Vasilissis Sofias Avenue, 11528 Athens, Greece
1 shared publication - Alexandros T. Tzallas
University of Ioannina
1 shared publication - Antonis-Marios Gkountis
Centre for Human Reproduction, Genesis Athens Thessaly, 41335 Larissa, Greece
1 shared publication - Athanasios Zachariou
Department of Urology, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece
1 shared publication - Athanasios Zikopoulos
Department of Reproductive Medicine and Surgery, University College London Hospitals NHS Foundation Trust, 235 Euston Road, London NW1 2BU, UK
1 shared publication - Efthalia Moustakli
Department of Nursing, School of Health Sciences, University of Ioannina, 4th kilometer National Highway Str. Ioannina-Athens, 45500 Ioannina, Greece
1 shared publication
Latest Publications
- Open Access
A Preliminary Machine Learning Assessment of Oxidation-Reduction Potential and Classical Sperm Parameters as Predictors of Sperm DNA Fragmentation Index
Background/Objectives: Traditional semen analysis techniques frequently result in incorrect male infertility diagnoses, despite advancements in assisted reproductive technology (ART). Reduced fertilization potential, decreased embryo development, and lower pregnancy success rates are associated with elevated DNA Fragmentation Index (DFI), which has been proposed as a diagnostic indicator of sperm DNA integrity. Improving reproductive outcomes requires incorporating DFI into predictive models due to its diagnostic importance. Methods: In this study, semen samples were stratified into low and high DFI groups across two datasets: the “Reference” dataset (162 samples) containing sperm motility (A, B, and C), total sperm count, and morphology percentage, and the “ORP” dataset (37 samples) with the same features plus oxidation-reduction potential (ORP). We trained and evaluated four machine learning (ML) models—Logistic Regression, Support Vector Machines (SVM), Bernoulli Naive Bayes (BNB), and Random Forest (RF)- using three feature subsets and three preprocessing techniques (Robust Scaling, Min-Max Scaling, and Standard Scaling). Results: Feature subset selection had a significant impact on model performance, with the full feature set (X_all) yielding the best results, and the combination of Robust and MinMax scaling forming the most effective preprocessing pipeline. Conclusions: ORP proved to be a critical feature, enhancing model generalization and prediction performance. These findings suggest that data enrichment, particularly with ORP, could enable the development of ML frameworks that improve prognostic precision and patient outcomes in ART.
Open AccessA Preliminary Machine Learning Assessment of Oxidation-Reduction Potential and Classical Sperm Parameters as Predictors of Sperm DNA Fragmentation Index
Background/Objectives: Traditional semen analysis techniques frequently result in incorrect male infertility diagnoses, despite advancements in assisted reproductive technology (ART). Reduced fertilization potential, decreased embryo development, and lower pregnancy success rates are associated with elevated DNA Fragmentation Index (DFI), which has been proposed as a diagnostic indicator of sperm DNA integrity. Improving reproductive outcomes requires incorporating DFI into predictive models due to its diagnostic importance. Methods: In this study, semen samples were stratified into low and high DFI groups across two datasets: the “Reference” dataset (162 samples) containing sperm motility (A, B, and C), total sperm count, and morphology percentage, and the “ORP” dataset (37 samples) with the same features plus oxidation-reduction potential (ORP). We trained and evaluated four machine learning (ML) models—Logistic Regression, Support Vector Machines (SVM), Bernoulli Naive Bayes (BNB), and Random Forest (RF)- using three feature subsets and three preprocessing techniques (Robust Scaling, Min-Max Scaling, and Standard Scaling). Results: Feature subset selection had a significant impact on model performance, with the full feature set (X_all) yielding the best results, and the combination of Robust and MinMax scaling forming the most effective preprocessing pipeline. Conclusions: ORP proved to be a critical feature, enhancing model generalization and prediction performance. These findings suggest that data enrichment, particularly with ORP, could enable the development of ML frameworks that improve prognostic precision and patient outcomes in ART.