Classification of the cardiotocogram data for anticipation of fetal risks using machine learning techniques

Sahin, H. Bahadir; Subaşı, Abdülhamit

doi:10.1016/j.asoc.2015.04.038

Cited by 102 publications

(44 citation statements)

References 34 publications

(34 reference statements)

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“…The data set was created automatically by the software called SisPorto 2.0 [13]. As seen in Table I, the suspected instances were excluded from the original data set because these cases do not have any contribution to establishing of the diagnosis [10]. The general features of the data set are shown in Table I.…”

Section: Data Collectionmentioning

confidence: 99%

“…* corresponding author; e-mail: comertzafer@gmail.com Ocak [9] has developed a medical decision support system based on SVM and genetic algorithm (GA). Sahin and Subasi [10] have compared the performances of eight different machine learning techniques by using WEKA software. As seen in mentioned works, some works focus on the classifying performance, whereas some of them try to find the most relevant features in order to reduce the dimension of feature space.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of Machine Learning Techniques for Fetal Heart Rate Classification

Cömert

Kocamaz

2017

Acta Phys. Pol. A

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Cardiotocography is a monitoring technique providing important and vital information on fetal status during antepartum and intrapartum periods. The advances in modern obstetric practice allowed many robust and reliable machine learning techniques to be utilized in classifying fetal heart rate signals. The role of machine learning approaches in diagnosing diseases is becoming increasingly essential and intertwined. The main aim of the present study is to determine the most efficient machine learning technique to classify fetal heart rate signals. Therefore, the research has been focused on the widely used and practical machine learning techniques, such as artificial neural network, support vector machine, extreme learning machine, radial basis function network, and random forest. In a comparative way, fetal heart rate signals were classified as normal or hypoxic using the aforementioned machine learning techniques. The performance metrics derived from confusion matrix were used to measure classifiers' success. According to experimental results, although all machine learning techniques produced satisfactory results, artificial neural network yielded the rather well results with the sensitivity of 99.73% and specificity of 97.94%. The study results show that the artificial neural network was superior to other algorithms.

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Section: Data Collectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparison of Machine Learning Techniques for Fetal Heart Rate Classification

Cömert

Kocamaz

2017

Acta Phys. Pol. A

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“…This technique uses to predict for crime incident by concentrate on geographical areas of concern. ANNs are the most widely implemented methods in forecasting building energy consumption [16].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…In order to achieve the best split, the question that separates the data into two homogeneous parts, after splitting the data into training and testing set, built a CART model without pruning on the training set and analyze the performance on the test set. CART uses Gini index to select the attribute which has maximum information [16].…”

Section: Classification and Regression Trees (Cart)mentioning

confidence: 99%

Classification Techniques: A Review

Kaur¹,

Verma²

2017

IOSR JCE

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“…However, the process is far from satisfactory. Most recent advances in pregnancy outcome prediction use data collected after mothers get pregnant [3][4][5]. In this situation, if an adverse pregnancy outcome is diagnosed, the parents will suffer physically, financially and emotionally.…”

Section: Introductionmentioning

confidence: 99%

Applying deep learning for adverse pregnancy outcome detection with pre-pregnancy health data

Feng

Yang

et al. 2018

MATEC Web Conf.

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Abstract. Adverse pregnancy outcomes can bring enormous losses to both families and the society. Thus, pregnancy outcome prediction stays a crucial research topic as it may help reducing birth defect and improving the quality of population. However, recent advances in adverse pregnancy outcome detection are driven by data collected after mothers having been pregnant. In this situation, if a bad pregnancy outcome is diagnosed, the parents will suffer both physically and emotionally. In this paper, we develop a deep learning algorithm which is able to detect and classify adverse pregnancy outcomes before parents getting pregnant. We train a multi-layer neural network by using a dataset of 75542 couples' multidimension pre-pregnancy health data. Our model outperforms some of algorithms in accuracy, recall and F1 score.

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Classification of the cardiotocogram data for anticipation of fetal risks using machine learning techniques

Cited by 102 publications

References 34 publications

Comparison of Machine Learning Techniques for Fetal Heart Rate Classification

Comparison of Machine Learning Techniques for Fetal Heart Rate Classification

Classification Techniques: A Review

Applying deep learning for adverse pregnancy outcome detection with pre-pregnancy health data

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