Fetal distress prediction using discriminant analysis, decision tree, and artificial neural network

Journals Menu

Follow SCIRP

	+1 323-425-8868
	customer@scirp.org
	+86 18163351462(WhatsApp)
	1655362766

	Paper Publishing WeChat

Journal of Biomedical Science and Engineering

ISSN Print: 1937-6871
ISSN Online: 1937-688X
www.scirp.org/journal/jbise
E-mail: jbise@scirp.org

Google-based Impact Factor: 0.66
Citations h5-index & Ranking

Journals Menu

"Fetal distress prediction using discriminant analysis, decision tree, and artificial neural network"
written by Mei-Ling Huang, Yung-Yan Hsu,
published by Journal of Biomedical Science and Engineering, Vol.5 No.9, 2012

has been cited by the following article(s):

Google Scholar
CrossRef

[1]	Fetal state health monitoring using novel Enhanced Binary Bat Algorithm
	Computers and Electrical Engineering, 2022

[2]	Comparative Analysis of Different Efficient Machine Learning Methods for Fetal Health Classification
	Applied Bionics and …, 2022

[3]	Investigating association relationship between fetal heart rate parameters from cardiotocography employing multi-objective evolutionary algorithms
	International Journal of Information …, 2022

[4]	Comparative Analysis of Machine Learning Models For Early Detection of Fetal Disease using Feature Extraction
	2022 IEEE 11th …, 2022

[5]	Evaluation of Diagnostic Performance of Machine Learning Algorithms to Classify the Fetal Heart Rate Baseline From Cardiotocograph
	International Journal of …, 2022

[6]	Optimized Classification of fetal state health using GWO and WOA
	2022 IEEE 9th International Conference …, 2022

[7]	A Comparative Analysis of Neural Network and Decision Tree Model for Detecting Result Anomalies
	Open Access Library Journal, 2022

[8]	Research Article Comparative Analysis of Different Efficient Machine Learning Methods for Fetal Health Classification
	2022

[9]	Deep Learning Classification of Fetal Cardiotocography Data with Differential Privacy
	2022 International Conference on …, 2022

[10]	Automatic detection of fetal health status from cardiotocography data using machine learning algorithms
	Journal of Bangladesh …, 2021

[11]	A comprehensive review of techniques for processing and analyzing fetal heart rate signals
	Sensors, 2021

[12]	An attention-based CNN-BiLSTM hybrid neural network enhanced with features of discrete wavelet transformation for fetal acidosis classification
	Expert Systems with Applications, 2021

[13]	Multiparametric investigation of dynamics in fetal heart rate signals
	Bioengineering, 2021

[14]	A Class Imbalance Monitoring Model for Fetal Heart Contractions Based on Gradient Boosting Decision Tree Ensemble Learning
	… Conference on Data Mining and Big …, 2021

[15]	Effective techniques for intelligent cardiotocography interpretation using XGB-RF feature selection and stacking fusion
	2021 IEEE …, 2021

[16]	Towards Making More Reliable Cardiotocogram Data Prediction with Limited Expert Knowledge: Exploiting Unlabeled Data with Semi-supervised Boosting Method
	… Conference on Data Mining and Big …, 2021

[17]	Extreme Learning Machines with feature selection using GA for effective prediction of fetal heart disease: A Novel Approach
	Informatica, 2021

[18]	Classification of Imbalanced Fetal Health Data by PSO Based Ensemble Recursive Feature Elimination ANN
	… Conference on Swarm …, 2021

[19]	Intelligent classification of antepartum cardiotocography model based on deep forest
	2021

[20]	Analysis and Interpretation of Uterine Contraction Signals Using Artificial Intelligence
	2021

[21]	An intelligent prenatal screening system for the prediction of Trisomy-21
	2021

[22]	Machine Learning based Emotion classification in the COVID-19 Real World Worry Dataset
	Computer Science, 2021

[23]	Data Mining for the Prediction of Fetal Malformation Through Cardiotocography Data
	2021

[24]	An Improved Decision Tree Classification Approach for Expectation of Cardiotocogram
	2021

[25]	Enhanced Classification Performance of Cardiotocogram Data for Fetal State Anticipation Using Evolutionary Feature Reduction Techniques
	2021

[26]	Automatic fetal distress assessment during labor based on modal and parametrical analysis of the cardiotocographic recording
	2020

[27]	Fusing fine-tuned deep features for recognizing different tympanic membranes
	2020

[28]	Classifying the type of delivery from cardiotocographic signals: a machine learning approach
	2020

[29]	Intelligent Antenatal Fetal Monitoring Model Based on Adaptive Neuro-Fuzzy Inference System Through
	2020

[30]	Ensemble based technique for the assessment of fetal health using cardiotocograph–a case study with standard feature reduction techniques
	2020

[31]	Classification of the Cardiotocogram Data for Anticipation of Fetal Risks using Bagging Ensemble Classifier
	2020

[32]	Classification and Feature Selection Approaches for Cardiotocography by Machine Learning Techniques
	2020

[33]	Fetal Health Status Classification Using MOGA-CD Based Feature Selection Approach
	2020

[34]	Predicting fetal hypoxia using common spatial pattern and machine learning from cardiotocography signals
	2020

[35]	Intelligent Antenatal Fetal Monitoring Model Based on Adaptive Neuro-Fuzzy Inference System Through Cardiotocography
	2020

[36]	Deteksi Dini Penyakit pada Daun Stroberi Berbasis Pengolahan Citra
	2020

[37]	Abnormal Foetuses Classification Based on Cardiotocography Recordings Using Machine Learning and Deep Learning Algorithms
	2020

[38]	Machine Learning Analysis for Remote Prenatal Care
	2020

[39]	Automatic Classification of Antepartum Cardiotocography Using Fuzzy Clustering and Adaptive Neuro-Fuzzy Inference System
	2020

[40]	Cardiotocography Class Status Prediction Using Machine Learning Techniques.
	2019

[41]	Analysis Of Cardiotocogram Data For Fetal Distress Determination
	Think India Journal, 2019

[42]	Classification of Elderly Group with Hypertension for Preventing Cardiovascular Disease Complication
	2019

[43]	Exploring Fetal Health Status Using an Association Based Classification Approach
	2019

[44]	О «Тектонических сдвигах»(или их отсутствии?) драйверов международной мощи и влияния: опыт дискриминантного анализа
	2019

[45]	Fuzzy classifier based on clustering with pairs of ε-hyperballs and its application to support fetal state assessment
	Expert Systems with Applications, 2019

[46]	Cardiotocography Analysis for Fetal State Classification Using Machine Learning Algorithms
	2019

[47]	Cardiotocography Class Status Prediction Using Machine Learning Techniques
	2019

[48]	Prediction of intrapartum fetal hypoxia considering feature selection algorithms and machine learning models
	2019

[49]	KLASIFIKASI DIAGNOSIS MELAHIRKAN DENGAN METODE SESAR MENGGUNAKAN NEURAL NETWORK
	2019

[50]	Профілактика дистресу плода у вагітних з гіпертензивними розладами
	2019

[51]	Efficacy of Machine Learning in Predicting the Kind of Delivery by Cardiotocography
	2019

[52]	Imbalanced Cardiotocography Multi-classification for Antenatal Fetal Monitoring Using Weighted Random Forest
	2019

[53]	Cardiotocographic Signal Feature Extraction Through CEEMDAN and Time-Varying Autoregressive Spectral-Based Analysis for Fetal Welfare Assessment
	2019

[54]	SEM-based study for interpretability of intelligent prenatal fetal monitoring models
	2019

[55]	Knowledge Management System for Fetal Movement during Pregnancy
	2018

[56]	Analysis of Effects of Most Influential Risk Factors on Gestational Diabetes Mellitus
	2018

[57]	Correlating cardiotocography, apgar score and cord blood ph after caesarean section for fetal distress at Rahima Moosa mother and child hospital
	Thesis, 2018

[58]	Cardiotocography Data Set Classification with Extreme Learning Machine
	International Conference on Advanced Technologies, Computer Engineering and Science, 2018

[59]	Automatic classification of fetal heart rate based on convolutional neural network
	2018

[60]	Detección difusa del estado de bienestar fetal anteparto en embarazos de alto riesgo
	2018

[61]	Prognostic model based on image-based time-frequency features and genetic algorithm for fetal hypoxia assessment
	Computers in Biology and Medicine, 2018

[62]	Prediction of Fetal Health State during Pregnancy: A Survey
	2018

[63]	Divide and conquer! Data-mining tools and sequential multivariate analysis to search for diagnostic morphological characters within a plant polyploid complex …
	PLOS ONE, 2018

[64]	Fetal health status prediction based on maternal clinical history using machine learning techniques
	Computer Methods and Programs in Biomedicine, 2018

[65]	تخمین وزن جنین به‌وسیله ارتفاع رحم با مدل‌های رگرسیون و درخت تصمیم در بیمارستان امام خمینی شهر اهواز در زمستان 1393‎
	2018

[66]	Estimation of fetal weight by fundal height measurement using regression and decision tree models in Ahwaz Imam Khomeini Hospital in winter 2014
	2018

[67]	5. Analysis of Effects of Most Influential Risk Factors on Gestational Diabetes Mellitus
	2018

[68]	Applying deep learning for adverse pregnancy outcome detection with pre-pregnancy health data
	2018 2nd International Conference on Material Engineering and Advanced Manufacturing Technology (MEAMT 2018), 2018

[69]	A study of artificial neural network training algorithms for classification of cardiotocography signals
	Bitlis Eren University Journal of Science and Technology, 2017

[70]	Modelling Characteristics of Eye Movement Analysis for stress detection–Performance Analysis using Decision tree approach
	International Journal of Engineering and Innovative Technology (IJEIT), 2017

[71]	Asian Research Consortium
	2017

[72]	Multi-label classification methods for improving comorbidities identification
	Computers in Biology and Medicine, 2017

[73]	Comparison of Machine Learning Techniques for Fetal Heart Rate Classification
	2017

[74]	Fetal State Assessment Based on Cardiotocography Parameters Using PCA and AdaBoost
	2017

[75]	A Hybrid Filter-Wrapper Attribute Reduction Approach For Fetal Risk Anticipation
	Asian Journal of Research in Social Sciences and Humanities, 2017

[76]	Cardiotocography-A Comparative Study between Support Vector Machine and Decision Tree Algorithms
	International Journal of Trend in Research and Development, 2017

[77]	Decision tree to analyze the cardiotocogram data for fetal distress determination
	2017

[78]	Kardiyotokografi işaretlerinin analizi ve makine öğrenmesi teknikleri ile sınıflandırılması
	2017

[79]	Opposition based firefly algorithm optimized feature subset selection approach for fetal risk anticipation
	2016

[80]	A Decision Support System for Determination of Fetal Well-Being from Cardiotocogram Data
	2016

[81]	KARDİOTOKOGRAM VERİSİNDEN FETAL İYİLİK HALİNİN BELİRLENMESİ İÇİN BİR KARAR DESTEK SİSTEMİ
	Uluda? University Journal of The Faculty of Engineering, 2016

[82]	ANALISA NILAI UJIAN MASUK STT WASTUKANCANA BERDASARKAN NILAI UJIAN NASIONAL MENGGUNAKAN METODE BACKPROPAGATION
	2016

[83]	Analytical Study of Selected Classification Algorithms for Clinical Dataset
	Indian Journal of Science and Technology, 2016

[84]	Improving Children Diagnostics by Efficient Multi-label Classification Method
	Information Technologies in Medicine, 2016

[85]	Enhanced Classification Accuracy for Cardiotocogram Data with Ensemble Feature Selection and Classifier Ensemble
	Journal of Computer and Communications, 2016

[86]	Classification of Cardiotocography Data with WEKA
	2016

[87]	Boosting backpropagation algorithm by stimulus-sampling: Application in computer-aided medical diagnosis
	Journal of Biomedical Informatics, 2016

[88]	OPPOSITION-BASED FIREFLY ALGORITHM OPTIMIZED FEATURE SUBSET SELECTION APPROACH FOR FETAL RISK ANTICIPATION
	An International Journal (MLAIJ) , 2016

[89]	Classification model for cardiotocographies
	2016

[90]	Application of Machine Learning Techniques to classify Fetal Hypoxia
	2016

[91]	Fetal State Assessment from Cardiotocogram Data Using Artificial Neural Networks
	Journal of Medical and Biological Engineering, 2016

[92]	Machine learning algorithms for the creation of clinical healthcare enterprise systems
	Enterprise Information Systems, 2016

[93]	Perception of Bankers on Factors Causing Non Performing Assets in the Indian Banking Sector
	Asian Journal of Research in Social Sciences and Humanities, 2016

[94]	Methods of classification of the cardiotocogram
	2016

[95]	Classification of the cardiotocogram data for anticipation of fetal risks using machine learning techniques
	Applied Soft Computing, 2015

[96]	Distance Measures in Bioinformatics
	2015

[97]	Genetic Algorithm based feature subset selection for fetal state classification
	Journal of Communications Technology, Electronics and Computer Science, 2015

[98]	Decision Trees Based Classification of Cardiotocograms Using Bagging Approach
	2015 13th International Conference on Frontiers of Information Technology (FIT), 2015

[99]	剖宫产术与阴道助产术在足月妊娠临产胎儿窘迫中的应用
	2014

[100]	Geographical Indications in India: Hitherto and Challenges
	Research journal of Pharmaceutical, Biological and Chemical Sciences, 2014

[101]	Human Fetus Health Classification on Cardiotocographic Data Using Random Forests
	Intelligent Data analysis and its Applications, Volume II. Springer International Publishing, 2014

[102]	Analysis of Cardiotocogram Data for Fetal Distress Determination by Decision Tree Based Adaptive Boosting Approach
	Journal of Computer and Communications, 2014

[103]	Classification of cardiotocography records by random forest
	Telecommunications and Signal Processing (TSP), 2013 36th International Conference on. IEEE, 2013

[1]	Artificial intelligence and machine learning in cardiotocography: A scoping review European Journal of Obstetrics & Gynecology and Reproductive Biology, 2023 DOI:10.1016/j.ejogrb.2022.12.008

[2]	Frontiers of ICT in Healthcare Lecture Notes in Networks and Systems, 2023 DOI:10.1007/978-981-19-5191-6_20

[3]	Proceedings of International Joint Conference on Advances in Computational Intelligence Algorithms for Intelligent Systems, 2023 DOI:10.1007/978-981-99-1435-7_6

[4]	Multimodal Deep Learning for Predicting Adverse Birth Outcomes Based on Early Labour Data Bioengineering, 2023 DOI:10.3390/bioengineering10060730

[5]	Advances in Communication, Devices and Networking Lecture Notes in Electrical Engineering, 2023 DOI:10.1007/978-981-99-1983-3_33

[6]	Comparative Analysis of Intrusion Detection Systems and Machine Learning-Based Model Analysis Through Decision Tree IEEE Access, 2023 DOI:10.1109/ACCESS.2023.3296444

[7]	Comparative Analysis of Intrusion Detection Systems and Machine Learning-Based Model Analysis Through Decision Tree IEEE Access, 2023 DOI:10.1109/ACCESS.2023.3296444

[8]	Classification of Fetal Status from Cardiotocogram Data by Using Machine Learning 2023 12th International Conference on Control, Automation and Information Sciences (ICCAIS), 2023 DOI:10.1109/ICCAIS59597.2023.10382381

[9]	Comparative Analysis of Different Efficient Machine Learning Methods for Fetal Health Classification Applied Bionics and Biomechanics, 2022 DOI:10.1155/2022/6321884

[10]	Comparative Analysis of Machine Learning Models For Early Detection of Fetal Disease using Feature Extraction 2022 IEEE 11th International Conference on Communication Systems and Network Technologies (CSNT), 2022 DOI:10.1109/CSNT54456.2022.9787635

[11]	Evaluation of Diagnostic Performance of Machine Learning Algorithms to Classify the Fetal Heart Rate Baseline From Cardiotocograph International Journal of Business Analytics, 2022 DOI:10.4018/IJBAN.292060

[12]	Optimized Classification of fetal state health using GWO and WOA 2022 IEEE 9th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT), 2022 DOI:10.1109/SETIT54465.2022.9875521

[13]	Fetal state health monitoring using novel Enhanced Binary Bat Algorithm Computers and Electrical Engineering, 2022 DOI:10.1016/j.compeleceng.2022.108035

[14]	Automated Classification of CTG signals using Deep Learning based Scalogram Analysis 2022 IEEE Global Conference on Computing, Power and Communication Technologies (GlobConPT), 2022 DOI:10.1109/GlobConPT57482.2022.9938304

[15]	WITHDRAWN: Multimodal deep learning for predicting adverse birth outcomes based on early labour data Intelligence-Based Medicine, 2022 DOI:10.1016/j.ibmed.2022.100084

[16]	Fetal state health monitoring using novel Enhanced Binary Bat Algorithm Computers and Electrical Engineering, 2022 DOI:10.1016/j.compeleceng.2022.108035

[17]	Evaluation of Diagnostic Performance of Machine Learning Algorithms to Classify the Fetal Heart Rate Baseline From Cardiotocograph International Journal of Business Analytics, 2022 DOI:10.4018/IJBAN.292060

[18]	An attention-based CNN-BiLSTM hybrid neural network enhanced with features of discrete wavelet transformation for fetal acidosis classification Expert Systems with Applications, 2021 DOI:10.1016/j.eswa.2021.115714

[19]	Data Mining and Big Data Communications in Computer and Information Science, 2021 DOI:10.1007/978-981-16-7476-1_37

[20]	Data Mining and Big Data Communications in Computer and Information Science, 2021 DOI:10.1007/978-981-16-7502-7_24

[21]	Multiparametric Investigation of Dynamics in Fetal Heart Rate Signals Bioengineering, 2021 DOI:10.3390/bioengineering9010008

[22]	Effective techniques for intelligent cardiotocography interpretation using XGB-RF feature selection and stacking fusion 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2021 DOI:10.1109/BIBM52615.2021.9669694

[23]	An attention-based CNN-BiLSTM hybrid neural network enhanced with features of discrete wavelet transformation for fetal acidosis classification Expert Systems with Applications, 2021 DOI:10.1016/j.eswa.2021.115714

[24]	Intelligent classification of antepartum cardiotocography model based on deep forest Biomedical Signal Processing and Control, 2021 DOI:10.1016/j.bspc.2021.102555

[25]	An attention-based CNN-BiLSTM hybrid neural network enhanced with features of discrete wavelet transformation for fetal acidosis classification Expert Systems with Applications, 2021 DOI:10.1016/j.eswa.2021.115714

[26]	Information Technology and Systems Advances in Intelligent Systems and Computing, 2021 DOI:10.1007/978-3-030-68418-1_7

[27]	Proceedings of International Conference on Computational Intelligence, Data Science and Cloud Computing Lecture Notes on Data Engineering and Communications Technologies, 2021 DOI:10.1007/978-981-33-4968-1_26

[28]	Advances in Swarm Intelligence Lecture Notes in Computer Science, 2021 DOI:10.1007/978-3-030-78811-7_29

[29]	A Comprehensive Review of Techniques for Processing and Analyzing Fetal Heart Rate Signals Sensors, 2021 DOI:10.3390/s21186136

[30]	Classifying the type of delivery from cardiotocographic signals: A machine learning approach Computer Methods and Programs in Biomedicine, 2020 DOI:10.1016/j.cmpb.2020.105712

[31]	Predicting fetal hypoxia using common spatial pattern and machine learning from cardiotocography signals Applied Acoustics, 2020 DOI:10.1016/j.apacoust.2020.107429

[32]	Classification of the Cardiotocogram Data for Anticipation of Fetal Risks using Bagging Ensemble Classifier Procedia Computer Science, 2020 DOI:10.1016/j.procs.2020.02.248

[33]	Fetal Health Status Classification Using MOGA - CD Based Feature Selection Approach 2020 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), 2020 DOI:10.1109/CONECCT50063.2020.9198377

[34]	Machine Learning Analysis for Remote Prenatal Care 2020 IEEE REGION 10 CONFERENCE (TENCON), 2020 DOI:10.1109/TENCON50793.2020.9293890

[35]	XV Mediterranean Conference on Medical and Biological Engineering and Computing – MEDICON 2019 IFMBE Proceedings, 2020 DOI:10.1007/978-3-030-31635-8_95

[36]	Fusing fine-tuned deep features for recognizing different tympanic membranes Biocybernetics and Biomedical Engineering, 2020 DOI:10.1016/j.bbe.2019.11.001

[37]	Ensemble based technique for the assessment of fetal health using cardiotocograph – a case study with standard feature reduction techniques Multimedia Tools and Applications, 2020 DOI:10.1007/s11042-020-08853-2

[38]	Classification of Elderly Group with Hypertension for Preventing Cardiovascular Disease Complication 2019 22nd International Symposium on Wireless Personal Multimedia Communications (WPMC), 2019 DOI:10.1109/WPMC48795.2019.9096081

[39]	Smart Health Lecture Notes in Computer Science, 2019 DOI:10.1007/978-3-030-34482-5_7

[40]	Exploring Fetal Health Status Using an Association Based Classification Approach 2019 International Conference on Information Technology (ICIT), 2019 DOI:10.1109/ICIT48102.2019.00036

[41]	Prediction of intrapartum fetal hypoxia considering feature selection algorithms and machine learning models Health Information Science and Systems, 2019 DOI:10.1007/s13755-019-0079-z

[42]	Cardiotocography Analysis for Fetal State Classification Using Machine Learning Algorithms 2019 International Conference on Computer Communication and Informatics (ICCCI), 2019 DOI:10.1109/ICCCI.2019.8822218

[43]	Automatic Classification of Fetal Heart Rate Based on Convolutional Neural Network IEEE Internet of Things Journal, 2019 DOI:10.1109/JIOT.2018.2845128

[44]	Cardiotocographic Signal Feature Extraction Through CEEMDAN and Time-Varying Autoregressive Spectral-Based Analysis for Fetal Welfare Assessment IEEE Access, 2019 DOI:10.1109/ACCESS.2019.2950798

[45]	Fetal health status prediction based on maternal clinical history using machine learning techniques Computer Methods and Programs in Biomedicine, 2018 DOI:10.1016/j.cmpb.2018.06.010

[46]	Divide and conquer! Data-mining tools and sequential multivariate analysis to search for diagnostic morphological characters within a plant polyploid complex (Veronica subsect. Pentasepalae, Plantaginaceae) PLOS ONE, 2018 DOI:10.1371/journal.pone.0199818

[47]	Prognostic model based on image-based time-frequency features and genetic algorithm for fetal hypoxia assessment Computers in Biology and Medicine, 2018 DOI:10.1016/j.compbiomed.2018.06.003

[48]	Applying deep learning for adverse pregnancy outcome detection with pre-pregnancy health data MATEC Web of Conferences, 2018 DOI:10.1051/matecconf/201818910014

[49]	Fuzzy classifier based on clustering with pairs of ε-hyperballs and its application to support fetal state assessment Expert Systems with Applications, 2018 DOI:10.1016/j.eswa.2018.09.030

[50]	Multi-label classification methods for improving comorbidities identification Computers in Biology and Medicine, 2017 DOI:10.1016/j.compbiomed.2017.07.006

[51]	Comparison of Machine Learning Techniques for Fetal Heart Rate Classification Acta Physica Polonica A, 2017 DOI:10.12693/APhysPolA.132.451

[52]	Fetal state assessment based on cardiotocography parameters using PCA and AdaBoost 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), 2017 DOI:10.1109/CISP-BMEI.2017.8302314

[53]	Decision tree to analyze the cardiotocogram data for fetal distress determination 2017 International Conference on Sustainable Information Engineering and Technology (SIET), 2017 DOI:10.1109/SIET.2017.8304182

[54]	A Study of Artificial Neural Network Training Algorithms for Classification of Cardiotocography Signals Bitlis Eren University Journal of Science and Technology, 2017 DOI:10.17678/beuscitech.338085

[55]	Enhanced Classification Accuracy for Cardiotocogram Data with Ensemble Feature Selection and Classifier Ensemble Journal of Computer and Communications, 2016 DOI:10.4236/jcc.2016.44003

[56]	Information Technologies in Medicine Advances in Intelligent Systems and Computing, 2016 DOI:10.1007/978-3-319-39796-2_21

[57]	Boosting backpropagation algorithm by stimulus-sampling: Application in computer-aided medical diagnosis Journal of Biomedical Informatics, 2016 DOI:10.1016/j.jbi.2016.08.004

[58]	Fetal State Assessment from Cardiotocogram Data Using Artificial Neural Networks Journal of Medical and Biological Engineering, 2016 DOI:10.1007/s40846-016-0191-3

[59]	Machine learning algorithms for the creation of clinical healthcare enterprise systems Enterprise Information Systems, 2016 DOI:10.1080/17517575.2016.1251617

[60]	KARDİOTOKOGRAM VERİSİNDEN FETAL İYİLİK HALİNİN BELİRLENMESİ İÇİN BİR KARAR DESTEK SİSTEMİ Uludağ University Journal of The Faculty of Engineering, 2016 DOI:10.17482/uumfd.278033

[61]	Classification of the cardiotocogram data for anticipation of fetal risks using machine learning techniques Applied Soft Computing, 2015 DOI:10.1016/j.asoc.2015.04.038

[62]	Decision Trees Based Classification of Cardiotocograms Using Bagging Approach 2015 13th International Conference on Frontiers of Information Technology (FIT), 2015 DOI:10.1109/FIT.2015.14

[63]	Intelligent Data analysis and its Applications, Volume II Advances in Intelligent Systems and Computing, 2014 DOI:10.1007/978-3-319-07773-4_19

[64]	Intelligent Data analysis and its Applications, Volume II Advances in Intelligent Systems and Computing, 2014 DOI:10.1007/978-3-319-07773-4_19

[65]	Analysis of Cardiotocogram Data for Fetal Distress Determination by Decision Tree Based Adaptive Boosting Approach Journal of Computer and Communications, 2014 DOI:10.4236/jcc.2014.29005

[66]	Classification of cardiotocography records by random forest 2013 36th International Conference on Telecommunications and Signal Processing (TSP), 2013 DOI:10.1109/TSP.2013.6614010

Follow SCIRP

	+1 323-425-8868
	customer@scirp.org
	+86 18163351462(WhatsApp)
	1655362766

	Paper Publishing WeChat

Journal of Biomedical Science and Engineering

Journal of Biomedical Science and Engineering

Journals Menu

Home

About SCIRP

Service

Policies