Explainable Computational Intelligence Model for Antepartum Fetal Monitoring to Predict the Risk of IUGR

Machine learning analysis of pregnancy data enables early identification of a subpopulation of newborns with ASD

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Explainable AI: A Review of Machine Learning Interpretability Methods

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Literature on Applied Machine Learning in Metagenomic Classification: A Scoping Review

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Identification of Latent Risk Clinical Attributes for Children Born Under IUGR Condition Using Machine Learning Techniques

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Big data and machine learning algorithms for health-care delivery

(2019) Kee Yuan Ngiam et al.   LANCET ONCOLOGY

Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners

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Integrating machine learning techniques and physiology based heart rate features for antepartum fetal monitoring

(2019) Maria G. Signorini et al.   COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE

A Comprehensive Feature Analysis of the Fetal Heart Rate Signal for the Intelligent Assessment of Fetal State

(2018) Zhidong Zhao et al.   Journal of Clinical Medicine

Antepartum fetal heart rate feature extraction and classification using empirical mode decomposition and support vector machine

(2011) Niranjana Krupa et al.   Biomedical Engineering Online

Intrauterine growth restriction (IUGR) risk decision based on support vector machines

(2011) Fikret Gürgen et al.   EXPERT SYSTEMS WITH APPLICATIONS