Artificial intelligence in prenatal diagnosis: Down syndrome risk assessment with the power of gradient boosting-based machine learning algorithms.
Objective One of the most common chromosomal abnormalities seen during pregnancy is Down syndrome (Trisomy 21). To determine the risk of Down syndrome, first-trimester combined screening tests are essential. Using data from the first-trimester screening test, this study compares machine learning and...
| Publicado en: | Turkish Journal of Obstetrics & Gynecology Vol. 22; no. 2; pp. 1 - 9 |
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| Autores principales: | , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Galenos Yayinevi Tic. LTD. STI
Jun2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=185875010&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 185875010 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 21499322 LTZI jtl: Turkish Journal of Obstetrics & Gynecology issn: 21499322 maglogo: N pubinfo: dt: Jun2025 vid: 22 iid: 2 pid: 28155 pub: Galenos Yayinevi Tic. LTD. STI artinfo: ui: 185875010 185875010 185875010 10.4274/tjod.galenos.2025.83278 185875010 ppf: 1 ppct: 8 formats: tig: atl: Artificial intelligence in prenatal diagnosis: Down syndrome risk assessment with the power of gradient boosting-based machine learning algorithms. aug: au: Yalçın, Emre Koç, Tarık Kaan Aslan, Serpil Demir, Süleyman Cansun Evrüke, İsmail Cüneyt Sucu, Mete Avan, Mesut Uzay, Fatma İşlek sug: subj: Boosting Machine Learning Algorithms Prenatal Diagnosis Diagnosis, Computer Assisted Artificial Intelligence Down Syndrome Diagnosis Risk Assessment Human Male Female Turkiye Pregnancy Comparative Studies Descriptive Statistics Classification Algorithms Unnecessary Procedures Decision Making, Clinical Male Female ab: Objective One of the most common chromosomal abnormalities seen during pregnancy is Down syndrome (Trisomy 21). To determine the risk of Down syndrome, first-trimester combined screening tests are essential. Using data from the first-trimester screening test, this study compares machine learning and deep learning models to forecast the risk of Down syndrome. Materials and Methods Within the scope of the study, biochemical and biophysical data of 959 pregnant women who underwent first-trimester screening tests at Çukurova University Obstetrics and Gynecology Clinic between 2020-2024 were analyzed. After cleaning missing and erroneous data, various preprocessing and normalization techniques were applied to the final dataset consisting of 853 observations. Down syndrome risk prediction was performed using different machine learning models, and model performances were compared based on accuracy rates and other evaluation metrics. Results Experimental results show that the CatBoost model provides the highest success rate, with an accuracy rate of 95.31%. In addition, the XGBoost and LightGBM models exhibited high performance, with accuracy rates of 95.19% and 94.84%, respectively. The study also examines the effects of the class imbalance problem on model performance in detail and evaluates various strategies to reduce this imbalance. Conclusion 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. Keywords:Down syndrome, first-trimester screening test, gradient boosting, machine learning, artificial intelligence, classification algorithms: pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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