Hyperparameter optimization based machine learning approach for early diagnosis of fetal genetic disorders

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dc.contributor.authorYalcin, Emre
dc.contributor.authorKoc, Tarik Kaan
dc.contributor.authorAslan, Serpil
dc.contributor.authorDemir, Suleyman Cansun
dc.contributor.authorAykut, Serdar
dc.contributor.authorSucu, Mete
dc.date.accessioned2026-06-19T06:41:07Z
dc.date.available2026-06-19T06:41:07Z
dc.date.issued2025
dc.departmentMalatya Turgut Özal Üniversitesi
dc.description.abstractPrenatal 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.
dc.identifier.doi10.1007/s10791-025-09815-8
dc.identifier.issn2948-2984
dc.identifier.issn2948-2992
dc.identifier.issue1
dc.identifier.orcid0000-0003-3818-6712
dc.identifier.orcid0000-0002-3317-0634
dc.identifier.orcid0000-0001-8009-063X
dc.identifier.orcid0000-0001-8331-9559
dc.identifier.orcid0000-0002-6889-7147
dc.identifier.scopus2-s2.0-105022620568
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1007/s10791-025-09815-8
dc.identifier.urihttps://hdl.handle.net/20.500.12899/6081
dc.identifier.volume28
dc.identifier.wosWOS:001620165000004
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofDiscover Computing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20260612
dc.subjectFirst Trimester Screening
dc.subjectDown Syndrome
dc.subjectMachine Learning
dc.subjectGpt-4O, Hyperparameter Tuning
dc.titleHyperparameter optimization based machine learning approach for early diagnosis of fetal genetic disorders
dc.typeArticle

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