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Artificial intelligence-driven predictive framework for early detection of still birth.

Sarah A Alzakari1, Asma Aldrees2, Muhammad Umer3

  • 1Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.

SLAS Technology
|October 18, 2024
PubMed
Summary

This study introduces a machine learning model, Tabular Prior Data Fitted Network (TabPFN), for stillbirth prediction. The AI model achieved 97.91% accuracy, offering a promising tool for early disease detection in prenatal care.

Keywords:
Artificial intelligenceCardiotocographyHealthcarePredictive modelingStillbirth predictionTabPFNWomen healthcare

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Area of Science:

  • Medical Informatics
  • Machine Learning in Healthcare
  • Predictive Analytics

Background:

  • Early disease detection is crucial for effective medical treatment.
  • Stillbirth prediction is a significant global health challenge despite advancements in prenatal care.
  • Machine learning (ML) offers potential for improving stillbirth prediction.

Purpose of the Study:

  • To investigate the efficiency of the Tabular Prior Data Fitted Network (TabPFN) model for stillbirth prediction.
  • To compare the performance of TabPFN against 13 other ML models.
  • To address the need for accurate and in-depth analysis in stillbirth prediction.

Main Methods:

  • Utilized the cardiotocography (CTG) dataset from the UCI ML repository.
  • Adopted the Tabular Prior Data Fitted Network (TabPFN) model.
  • Evaluated model performance using precision, recall, F-score, MCC, and AUC, with k-fold cross-validation.

Main Results:

  • The TabPFN model achieved 97.91% accuracy in predicting stillbirth.
  • Specific evaluation metrics included 97.87% precision, 98.26% recall, 98.05% F-score, 96.42% MCC, and 98.88% AUC.
  • Performance comparison demonstrated the superior efficacy of the TabPFN model over other state-of-the-art studies.

Conclusions:

  • The TabPFN model shows superior performance for stillbirth prediction.
  • This AI-driven approach can significantly contribute to reducing stillbirth rates.
  • Further research and intervention using advanced ML models are warranted.