Artificial intelligence in prenatal diagnosis: Down syndrome risk assessment with the power of gradient boosting-based machine learning algorithms
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.
Authors
- Mete Sucu (ORCID: https://orcid.org/0000-0002-6889-7147)
- Tarık Kaan Koç (ORCID: https://orcid.org/0009-0008-8874-3904)
- Serpil Aslan (ORCID: https://orcid.org/0000-0001-8009-063X)
- Fatma İşlek Uzay (ORCID: https://orcid.org/0000-0002-8660-4854)
- Cüneyt Evrüke (ORCID: https://orcid.org/0000-0002-7348-6457)
- Emre Yalçın (ORCID: https://orcid.org/0000-0001-7088-6749)
- Mesut Avan (ORCID: https://orcid.org/0009-0001-8458-6963)
- Cansun Demir (ORCID: https://orcid.org/0000-0001-8331-9559)
Institutions
- Malatya Turgut Özal Üniversitesi (TR)
- Cukurova University (TR)
- Turgut Özal University (TR)
Publication Details
- Journal
- Journal of Turkish Society of Obstetric and Gynecology
- Published
- 2025-05-27
- DOI
- https://doi.org/10.4274/tjod.galenos.2025.83278
- Citations
- 2
- Primary Topic
- Healthcare Systems and Public Health
- Type
- article
- Field-Weighted Citation Impact
- 2.71