Prediction of Intrapartum Hypoxia from Cardiotocography Data Using Machine Learning

Fergus, Paul; Huang, De-Shuang; Hamdan, Hani

doi:10.1016/b978-0-12-803468-2.00006-0

Cited by 9 publications

(6 citation statements)

References 111 publications

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“…Because sensitivity concentrates on the actual positives that are correctly predicted and since our data set is highly imbalanced (9% negative class and 91% positive class), one can achieve very high sensitivity by predicting all the cases to be a positive class. In other words, classifiers are biased towards detecting the majority class and less sensitive to the minority class, and this leads to bias in classification [49], [50]. In such highly imbalanced data, the main interest will be to correctly classify the minority class.…”

Section: Resultsmentioning

confidence: 99%

Lightweight Convolutional Neural Network for Breast Cancer Classification Using RNA-Seq Gene Expression Data

et al. 2019

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Gene expressions are considered among the most used features in cancer classification. The available gene expression data has a small number of samples and a relatively big number of dimensions, and that makes it not suitable for deep Convolutional Neural Networks (CNN) architectures, which exhibit state-of-the-art performance in many fields. In this paper, we propose a lightweight CNN architecture for breast cancer classification using gene expression data downloaded from Pan-Cancer Atlas using ''Illumina HiSeq'' platform. The downloaded gene expression data is preprocessed and then transformed into 2Dimages. We started the preprocessing by removing the outlier samples, which are determined based on the Array-Array Intensity Correlation (AAIC), which defines a symmetric square matrix of Spearman correlation. Then we applied a normalization process on the gene expression data to ensure that we can infer the expression level from it correctly and avoid biases in the expression measures. Finally, filtering is applied on the data. Model selection or a parameters search strategy is conducted to choose the values of the CNN hyper-parameters that give optimal performance. Our experiments show that our proposed method achieves an accuracy of 98.76%, which is the highest compared to other competing methods.INDEX TERMS Tumor type classification, RNA-Seq, gene expression, convolutional neural network, edge detection.MURTADA K. ELBASHIR received the B.Sc. degree (Hons.) in computer/statistics from the University of Gezira, Wad Madani, Sudan, in 2000, the M.Sc. degree (Hons.) in computer information systems from the University of the Free State, Bloemfontein, South Africa, in 2003, and the Ph.D. degree in computer science and technology

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Section: Resultsmentioning

confidence: 99%

Lightweight Convolutional Neural Network for Breast Cancer Classification Using RNA-Seq Gene Expression Data

et al. 2019

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“…An essential diagnostic tool, CXR [ 157 – 159 ], and CT scans [ 160 ] provide a fast and easily accessible overview of the chest’s internal structures like the heart, lungs, ribs, and diaphragm. Moreover, few studies [ 137 – 142 ] used cough sounds for the identification of several chest diseases.…”

Section: Resultsmentioning

confidence: 99%

“…When imbalanced datasets are supplied, one class will have the majority of the instances, while the other classes will only have a small number of instances among them. This results in an uneven distribution of classes and the incorrect categorization of examples belonging to minority groups since the classifier system tends to be biased and promotes cases belonging to the majority [ 137 ]. It has been observed that (see Tables 2 & 3 ) most of the lung disease classes of the CXR, CT scan, and cough sound datasets are imbalanced.…”

Section: Methodsmentioning

confidence: 99%

Multi-modal deep learning methods for classification of chest diseases using different medical imaging and cough sounds

Malik,

Anees

2024

PLoS ONE

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Chest disease refers to a wide range of conditions affecting the lungs, such as COVID-19, lung cancer (LC), consolidation lung (COL), and many more. When diagnosing chest disorders medical professionals may be thrown off by the overlapping symptoms (such as fever, cough, sore throat, etc.). Additionally, researchers and medical professionals make use of chest X-rays (CXR), cough sounds, and computed tomography (CT) scans to diagnose chest disorders. The present study aims to classify the nine different conditions of chest disorders, including COVID-19, LC, COL, atelectasis (ATE), tuberculosis (TB), pneumothorax (PNEUTH), edema (EDE), pneumonia (PNEU). Thus, we suggested four novel convolutional neural network (CNN) models that train distinct image-level representations for nine different chest disease classifications by extracting features from images. Furthermore, the proposed CNN employed several new approaches such as a max-pooling layer, batch normalization layers (BANL), dropout, rank-based average pooling (RBAP), and multiple-way data generation (MWDG). The scalogram method is utilized to transform the sounds of coughing into a visual representation. Before beginning to train the model that has been developed, the SMOTE approach is used to calibrate the CXR and CT scans as well as the cough sound images (CSI) of nine different chest disorders. The CXR, CT scan, and CSI used for training and evaluating the proposed model come from 24 publicly available benchmark chest illness datasets. The classification performance of the proposed model is compared with that of seven baseline models, namely Vgg-19, ResNet-101, ResNet-50, DenseNet-121, EfficientNetB0, DenseNet-201, and Inception-V3, in addition to state-of-the-art (SOTA) classifiers. The effectiveness of the proposed model is further demonstrated by the results of the ablation experiments. The proposed model was successful in achieving an accuracy of 99.01%, making it superior to both the baseline models and the SOTA classifiers. As a result, the proposed approach is capable of offering significant support to radiologists and other medical professionals.

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“…Furthermore, a study by Francis et al [37] recommended utilizing the Apgar score in the ML model as a hypoxia measure. The study's primary objective was to use ML algorithms to identify fetal hypoxia at delivery.…”

Section: Fetal Hypoxia During Labor Using DLmentioning

confidence: 99%

Fetal Hypoxia Detection Using Machine Learning: A Narrative Review

Alharbi,

Youldash,

Alotaibi

et al. 2024

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Fetal hypoxia is a condition characterized by a lack of oxygen supply in a developing fetus in the womb. It can cause potential risks, leading to abnormalities, birth defects, and even mortality. Cardiotocograph (CTG) monitoring is among the techniques that can detect any signs of fetal distress, including hypoxia. Due to the critical importance of interpreting the results of this test, it is essential to accompany these tests with the evolving available technology to classify cases of hypoxia into three cases: normal, suspicious, or pathological. Furthermore, Machine Learning (ML) is a blossoming technique constantly developing and aiding in medical studies, particularly fetal health prediction. Notwithstanding the past endeavors of health providers to detect hypoxia in fetuses, implementing ML and Deep Learning (DL) techniques ensures more timely and precise detection of fetal hypoxia by efficiently and accurately processing complex patterns in large datasets. Correspondingly, this review paper aims to explore the application of artificial intelligence models using cardiotocographic test data. The anticipated outcome of this review is to introduce guidance for future studies to enhance accuracy in detecting cases categorized within the suspicious class, an aspect that has encountered challenges in previous studies that holds significant implications for obstetricians in effectively monitoring fetal health and making informed decisions.

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Prediction of Intrapartum Hypoxia from Cardiotocography Data Using Machine Learning

Cited by 9 publications

References 111 publications

Lightweight Convolutional Neural Network for Breast Cancer Classification Using RNA-Seq Gene Expression Data

Lightweight Convolutional Neural Network for Breast Cancer Classification Using RNA-Seq Gene Expression Data

Multi-modal deep learning methods for classification of chest diseases using different medical imaging and cough sounds

Fetal Hypoxia Detection Using Machine Learning: A Narrative Review

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