{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T11:50:22Z","timestamp":1779364222332,"version":"3.53.0"},"reference-count":33,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,5,10]],"date-time":"2023-05-10T00:00:00Z","timestamp":1683676800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/diagnostics13101690","type":"journal-article","created":{"date-parts":[[2023,5,11]],"date-time":"2023-05-11T01:37:09Z","timestamp":1683769029000},"page":"1690","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Fetal Health State Detection Using Interval Type-2 Fuzzy Neural Networks"],"prefix":"10.3390","volume":"13","author":[{"given":"Rahib","family":"Abiyev","sequence":"first","affiliation":[{"name":"Applied Artificial Intelligence Research Centre, Department of Computer Engineering, Near East University, Nicosia 99138, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John Bush","family":"Idoko","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Near East University, Nicosia 99138, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0502-5827","authenticated-orcid":false,"given":"Hamit","family":"Alt\u0131parmak","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Near East University, Nicosia 99138, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Murat","family":"T\u00fcz\u00fcnkan","sequence":"additional","affiliation":[{"name":"Applied Artificial Intelligence Research Centre, Near East University, Nicosia 99138, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,10]]},"reference":[{"key":"ref_1","unstructured":"(2023, January 24). 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