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Dergiler / International Scientific and Vocational Studies Journal / 2019 / Cilt: 3 - Sayı: 2

Classification of Cardiotocography Records with Naïve Bayes

Emre AVUÇLU
Abdullah ELEN
Sayfa
105–110
Yayın Tarihi
2019-12-31
DOI
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

The obtained data were presented in detail in the following sections.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Cardiotocography provides information about the fetal heart rate during pregnancy and childbirth, monitoring the uterine contractions and the physiological status of the fetus to identify hypoxia.
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Temel Bulgular & Veri:Accurate information from these records can be used to estimate the pathological condition of the fetus.
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Sonuç & Katkı:The obtained data were presented in detail in the following sections.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Biomedical diagnostics · Machine learning algorithms · Fetal heart rate measurements

Özet

Cardiotocography provides information about the fetal heart rate during pregnancy and childbirth, monitoring the uterine contractions and the physiological status of the fetus to identify hypoxia. Accurate information from these records can be used to estimate the pathological condition of the fetus. Thus, it allows early intervention by reporting any irreversible negative condition in the fetus. In this study, due to the importance of this subject, Naive Bayes machine learning algorithm can be used to diagnose the model developed. The result was 97.18% classification and 95.68% test success with Naive Bayes machine learning algorithm. The obtained data were presented in detail in the following sections.

Abstract

Cardiotocography provides information about the fetal heart rate during pregnancy and childbirth, monitoring the uterine contractions and the physiological status of the fetus to identify hypoxia. Accurate information from these records can be used to estimate the pathological condition of the fetus. Thus, it allows early intervention by reporting any irreversible negative condition in the fetus. In this study, due to the importance of this subject, Naive Bayes machine learning algorithm can be used to diagnose the model developed. The result was 97.18% classification and 95.68% test success with Naive Bayes machine learning algorithm. The obtained data were presented in detail in the following sections.

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Bu Makaleye Atıf Yap

AVUÇLU, E., ELEN, A. (2019). Classification of Cardiotocography Records with Naïve Bayes. International Scientific and Vocational Studies Journal, Cilt: 3 - Sayı: 2, 105–110. https://ajindex.com/articles/769807

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