Machine Learning Models for the Prediction of Preterm Birth at Mid-Gestation Using Individual Characteristics and Biophysical Markers: A Cohort Study

Models using routine mid-gestation data demonstrate effective prediction for iatrogenic PTB, with accuracy improving for earlier, more severe cases. In contrast, performance for spontaneous PTB was modest. Traditional Logistic Regression performed comparably to complex machine learning algorithms, highlighting that the clinical value is rooted in the subtype-specific modeling approach rather than in algorithmic complexity.

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Publication Details

Journal
Children
Published
2025-10-25
DOI
https://doi.org/10.3390/children12111451
Primary Topic
Preterm Birth and Chorioamnionitis
Type
article
Field-Weighted Citation Impact
0.00
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article

Machine Learning Models for the Prediction of Preterm Birth at Mid-Gestation Using Individual Characteristics and Biophysical Markers: A Cohort Study

Ιoannis Tsakiridis, Dimitra Kappou, Ioannis Papastefanou, Themistoklis Dagklis et al.
Children
Preterm Birth and Chorioamnionitis
article

Machine Learning Models for the Prediction of Preterm Birth at Mid-Gestation Using Individual Characteristics and Biophysical Markers: A Cohort Study

Ιoannis Tsakiridis, Dimitra Kappou, Ioannis Papastefanou, Themistoklis Dagklis, Apostolos Mamopoulos, Antonios Siargkas
article en

Abstract

Models using routine mid-gestation data demonstrate effective prediction for iatrogenic PTB, with accuracy improving for earlier, more severe cases. In contrast, performance for spontaneous PTB was modest. Traditional Logistic Regression performed comparably to complex machine learning algorithms, highlighting that the clinical value is rooted in the subtype-specific modeling approach rather than in algorithmic complexity.

ChildrenVol. 12(11)
King's College London (GB), Aristotle University of Thessaloniki (GR), IASO General Hospital (GR)
Openalex Percentile: Top 59%
Preterm Birth and Chorioamnionitis
DOIOpen Access PDFOpenAlex
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