ArticleDiagnostics (Basel, Switzerland)2023
Fetal Health State Detection Using Interval Type-2 Fuzzy Neural Networks.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Fetal health state detection method based on parameters efficient ensembling of deep learning.Frontiers in public health · 2026Article
- Fetal Health Diagnosis Based on Adaptive Dynamic Weighting with Main-Auxiliary Correction Network.Biotech (Basel (Switzerland)) · 2025Article
- BrainNet: an automated approach for brain stress prediction utilizing electrodermal activity signal with XLNet model.Frontiers in computational neuroscience · 2024Article
- Automated approach for fetal and maternal health management using light gradient boosting model with SHAP explainable AI.Frontiers in public health · 2024Article
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4 authors.
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Abstract
Diagnosis of fetal health is a difficult process that depends on various input factors. Depending on the values or the interval of values of these input symptoms, the detection of fetal health status is implemented. Sometimes it is difficult to determine the exact values of the intervals for diagnosing the diseases and there may always be disagreement between the expert doctors. As a result, the diagnosis of diseases is often carried out in uncertain conditions and can sometimes cause undesirable errors. Therefore, the vague nature of diseases and incomplete patient data can lead to uncertain decisions. One of the effective approaches to solve such kind of problem is the use of fuzzy logic in the construction of the diagnostic system. This paper proposes a type-2 fuzzy neural system (T2-FNN) for the detection of fetal health status. The structure and design algorithms of the T2-FNN system are presented. Cardiotocography, which provides information about the fetal heart rate and uterine contractions, is employed for monitoring fetal status. Using measured statistical data, the design of the system is implemented. Comparisons of various models are presented to prove the effectiveness of the proposed system. The system can be utilized in clinical information systems to obtain valuable information about fetal health status.
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