Automatic detection of fetal health status from cardiotocography data using machine learning algorithms

Rayhana, Md Tamjid; Arefina, ASM Shamsul; Chowdhury, Sabbir Ahmed

doi:10.3329/jbas.v45i2.57206

Cited by 2 publications

(2 citation statements)

References 11 publications

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“… 47 In a separate study, algorithms like logistic regression, random forest classifier, extreme gradient boosting approaches have autonomously determined fetal health status and have demonstrated an accuracy of 96.7% and an F1-Score of 0.963 in the pathologic class. 51 This study employed five ML algorithms to construct computation-friendly multiclass classification models for predicting fetal health. These models were trained using secondary cardiotocography datasets comprising normal, suspected, and pathological cases obtained from the University of California Irvine ML Repository.…”

Section: Ai For Fetal Monitoringmentioning

confidence: 99%

A Theoretical Exploration of Artificial Intelligence’s Impact on Feto-Maternal Health from Conception to Delivery

Yaseen,

Rather

2024

IJWH

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The implementation of Artificial Intelligence (AI) in healthcare is enhancing diagnostic accuracy in clinical setups. The use of AI in healthcare is steadily increasing with advancing technology, extending beyond disease diagnosis to encompass roles in fetomaternal health. AI harnesses Machine Learning (ML), Natural Language Processing (NLP), Artificial Neural Networks (ANN), and computer vision to analyze data and draw conclusions. Considering maternal health, ML analyzes vast datasets to predict maternal and fetal health outcomes, while NLP interprets medical texts and patient records to assist in diagnosis and treatment decisions. ANN models identify patterns in complex feto-maternal medical data, aiding in risk assessment and intervention planning whereas, computer vision enables the analysis of medical images for early detection of feto-maternal complications. AI facilitates early pregnancy detection, genetic screening, and continuous monitoring of maternal health parameters, providing real-time alerts for deviations, while also playing a crucial role in the early detection of fetal abnormalities through enhanced ultrasound imaging, contributing to informed decision-making. This review investigates into the application of AI, particularly through predictive models, in addressing the monitoring of feto-maternal health. Additionally, it examines potential future directions and challenges associated with these applications.

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Section: Ai For Fetal Monitoringmentioning

confidence: 99%

A Theoretical Exploration of Artificial Intelligence’s Impact on Feto-Maternal Health from Conception to Delivery

Yaseen,

Rather

2024

IJWH

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“…Machine learning methods were used in the research of Md. Tamjid Rayhan, et al on the automatic diagnosis of fetal health status using cardiotocography data [10]. In their study of five different machine learning algorithms, Eva Malacova and Sawitchaya Tippaya classified binary data as childbirth vs. live delivery.…”

Section: Related Workmentioning

confidence: 99%

Impact of Cross-Validation on Machine Learning Models for Early Detection of Intrauterine Fetal Demise

et al. 2023

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Intrauterine fetal demise in women during pregnancy is a major contributing factor in prenatal mortality and is a major global issue in developing and underdeveloped countries. When an unborn fetus passes away in the womb during the 20th week of pregnancy or later, early detection of the fetus can help reduce the chances of intrauterine fetal demise. Machine learning models such as Decision Trees, Random Forest, SVM Classifier, KNN, Gaussian Naïve Bayes, Adaboost, Gradient Boosting, Voting Classifier, and Neural Networks are trained to determine whether the fetal health is Normal, Suspect, or Pathological. This work uses 22 features related to fetal heart rate obtained from the Cardiotocogram (CTG) clinical procedure for 2126 patients. Our paper focuses on applying various cross-validation techniques, namely, K-Fold, Hold-Out, Leave-One-Out, Leave-P-Out, Monte Carlo, Stratified K-fold, and Repeated K-fold, on the above ML algorithms to enhance them and determine the best performing algorithm. We conducted exploratory data analysis to obtain detailed inferences on the features. Gradient Boosting and Voting Classifier achieved 99% accuracy after applying cross-validation techniques. The dataset used has the dimension of 2126 × 22, and the label is multiclass classified as Normal, Suspect, and Pathological condition. Apart from incorporating cross-validation strategies on several machine learning algorithms, the research paper focuses on Blackbox evaluation, which is an Interpretable Machine Learning Technique used to understand the underlying working mechanism of each model and the means by which it picks features to train and predict values.

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Automatic detection of fetal health status from cardiotocography data using machine learning algorithms

Cited by 2 publications

References 11 publications

A Theoretical Exploration of Artificial Intelligence’s Impact on Feto-Maternal Health from Conception to Delivery

A Theoretical Exploration of Artificial Intelligence’s Impact on Feto-Maternal Health from Conception to Delivery

Impact of Cross-Validation on Machine Learning Models for Early Detection of Intrauterine Fetal Demise

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