Journal of Computer and Communications

Journal of Computer and Communications

ISSN Print: 2327-5219
ISSN Online: 2327-5227
www.scirp.org/journal/jcc
E-mail: jcc@scirp.org
"Analysis of Cardiotocogram Data for Fetal Distress Determination by Decision Tree Based Adaptive Boosting Approach"
written by Esra Mahsereci Karabulut, Turgay Ibrikci,
published by Journal of Computer and Communications, Vol.2 No.9, 2014
has been cited by the following article(s):
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[2] Fetal Health Status Classification Using MOGA-CD Based Feature Selection Approach
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[3] Intelligent Antenatal Fetal Monitoring Model Based on Adaptive Neuro-Fuzzy Inference System Through Cardiotocography
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[4] Predicting Ayurveda-Based Constituent Balancing in Human Body Using Machine Learning Methods
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[5] Decision Tree Method Using for Fetal State Classification from Cardiotography Data
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[6] Intelligent Antenatal Fetal Monitoring Model Based on Adaptive Neuro-Fuzzy Inference System Through
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[7] An Integrated Firefly Algorithm with K-Nearest Neighbor for Cardiotocography Classification
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[10] Exploring Fetal Health Status Using an Association Based Classification Approach
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[11] The Application of Machine Learning Models in Fetal State Auto-Classification Based on Cardiotocograms
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[12] Cardiotocography Class Status Prediction Using Machine Learning Techniques
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[13] A K-means Interval Type-2 Fuzzy Neural Network for Medical Diagnosis
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[14] SEM-based study for interpretability of intelligent prenatal fetal monitoring models
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[15] Modelling Segmented Cardiotocography Time-Series Signals Using One-Dimensional Convolutional Neural Networks for the Early Detection of Abnormal Birth …
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[18] Imbalanced Cardiotocography Multi-classification for Antenatal Fetal Monitoring Using Weighted Random Forest
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[19] Prediction of Fetal Distress Using Linear and Non-linear Features of CTG Signals
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[20] Modeling fetal morphologic patterns through cardiotocography data: Decision tree-based approach
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[21] Cardiotocography Data Set Classification with Extreme Learning Machine
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[22] Cost-sensitive and hybrid-attribute measure multi-decision tree over imbalanced data sets
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[23] Machine learning ensemble modelling to classify caesarean section and vaginal delivery types using Cardiotocography traces
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[24] A study of artificial neural network training algorithms for classification of cardiotocography signals
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[25] Modelling Characteristics of Eye Movement Analysis for stress detection–Performance Analysis using Decision tree approach
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[26] A Bio Inspired Approach for Cardiotocogram Data Classification
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[27] Classification of caesarean section and normal vaginal deliveries using foetal heart rate signals and advanced machine learning algorithms
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[28] Modelling Fetal Morphologic Patterns Through Cardiotocography Data: Decision Tree Based Approach
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[29] Decision tree to analyze the cardiotocogram data for fetal distress determination
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[30] Application of Machine Learning Techniques to classify Fetal Hypoxia
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[31] Methods of classification of the cardiotocogram
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[32] Enhanced Classification Accuracy for Cardiotocogram Data with Ensemble Feature Selection and Classifier Ensemble
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[33] Classification model for cardiotocographies
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[34] Fetal State Assessment from Cardiotocogram Data Using Artificial Neural Networks
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[35] A Decision Support System for Determination of Fetal Well-Being from Cardiotocogram Data
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[36] KARDİOTOKOGRAM VERİSİNDEN FETAL İYİLİK HALİNİN BELİRLENMESİ İÇİN BİR KARAR DESTEK SİSTEMİ
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[37] ALGORITMA C4. 5 BERBASIS DECISION TREE UNTUK PREDIKSI KELAHIRAN BAYI PREMATUR
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[38] Effectiveness of an Education Program Concerning Cardiotocography on Nurse-Midwife's knowledge in Maternity Hospitals at Baghdad City
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[39] Decision Trees Based Classification of Cardiotocograms Using Bagging Approach
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[40] Classification of cardiotocograms using random forest classifier and selection of important features from cardiotocogram signal
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[41] Effectiveness of an education program concerning cardiotocography on nurse-midwife's knowledge in maternity hospitals at Baghdad City'
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[42] Geographical Indications in India: Hitherto and Challenges
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