Cardiotocography measures the fetal heart rate in the fetus during pregnancy to ensure physical health because cardiotocography gives data about fetal heart rate and uterine shrinkages which is very beneficial to dete...Cardiotocography measures the fetal heart rate in the fetus during pregnancy to ensure physical health because cardiotocography gives data about fetal heart rate and uterine shrinkages which is very beneficial to detect whether the fetus is normal or suspect or pathologic.Various cardiotocography measures infer wrongly and give wrong predictions because of human error.The traditional way of reading the cardiotocography measures is the time taken and belongs to numerous human errors as well.Fetal condition is very important to measure at numerous stages and give proper medications to the fetus for its well-being.In the current period Machine learning(ML)is a well-known classification strategy used in the biomedical field on various issues because ML is very fast and gives appropriate results that are better than traditional results.ML techniques play a pivotal role in detecting fetal disease in its early stages.This research article uses Federated machine learning(FML)and ML techniques to classify the condition of the fetus.This study proposed a model for the detection of bio-signal cardiotocography that uses FML and ML techniques to train and test the data.So,the proposed model of FML used numerous data preprocessing techniques to overcome data deficiency and achieves 99.06%and 0.94%of prediction accuracy and misprediction rate,respectively,and parallel the proposed model applying K-nearest neighbor(KNN)and achieves 82.93%and 17.07%of prediction accuracy and misprediction accuracy,respectively.So,by comparing both models FML outperformed the KNN technique and achieved the best and most appropriate prediction results as compared with previous studies the proposed study achieves the best and most accurate results.展开更多
Reducing neonatal mortality is a critical global health objective,especially in resource-constrained developing countries.This study employs machine learning(ML)techniques to predict fetal health status based on cardi...Reducing neonatal mortality is a critical global health objective,especially in resource-constrained developing countries.This study employs machine learning(ML)techniques to predict fetal health status based on cardiotocography(CTG)examination findings,utilizing a dataset from the Kaggle repository due to the limited comprehensive healthcare data available in developing nations.Features such as baseline fetal heart rate,uterine contractions,and waveform characteristics were extracted using the RFE wrapper feature engineering technique and scaled with a standard scaler.Six ML models—Logistic Regression(LR),Decision Tree(DT),Random Forest(RF),Gradient Boosting(GB),Categorical Boosting(CB),and Extended Gradient Boosting(XGB)—are trained via cross-validation and evaluated using performance metrics.The developed models were trained via cross-validation and evaluated using ML performance metrics.Eight out of the 21 features selected by GB returned their maximum Matthews Correlation Coefficient(MCC)score of 0.6255,while CB,with 20 of the 21 features,returned the maximum and highest MCC score of 0.6321.The study demonstrated the ability of ML models to predict fetal health conditions from CTG exam results,facilitating early identification of high-risk pregnancies and enabling prompt treatment to prevent severe neonatal outcomes.展开更多
Introduction: Labour admission cardiotocography (CTG) is commonly used non-invasive method of fetal monitoring in Sri Lanka. It may have a potentialto predict perinatal outcome in low-risk term pregnancies. Objectives...Introduction: Labour admission cardiotocography (CTG) is commonly used non-invasive method of fetal monitoring in Sri Lanka. It may have a potentialto predict perinatal outcome in low-risk term pregnancies. Objectives: Objectives of the study were to determine the perinatal outcomes of normal, suspicious and pathological admission CTGs and role of labour admission cardiotocography as a predictive test for perinatal outcome in low-risk term pregnancies in spontaneous labour. Methods: This study was a prospective observational study done involving 445 low risk, term pregnancies in spontaneous labour. Labour admission CTG was performed in each pregnancy and categorized into normal, suspicious and pathological CTG according to criteria depicted by National Institute of Clinical Excellence (NICE) guideline 2007. Apgar score less than 7 at five minutes, resuscitation at birth, admission to neonatal intensive care unit (NICU), seizure within first 24 hours of birth and meconium-stained amniotic fluid were the primary outcome measures to assess fetal asphyxia. Mode of delivery in each category, nuchal cord at birth were also assessed. Results: Majority of participants were in 25-to-29-year age group and were nulliparous. Frequencies of normal, suspicious and pathological CTG were 74.8%, 18% and 7.2% respectively. Pathological CTG was significantly associated with low Apgar score compared to non-pathological CTG group (p 0.005) while other outcome measures were not significant. Rate of operative delivery was 68% in pathological group and 20.8% in non-pathological CTG group. Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of labour admission CTG to detect fetal asphyxia were 51.85%, 95.69%, 43.75% and 96.85% respectively. Conclusions: Incidence of pathological labour admission CTG was 7.2%. Apgar score less than 7 at five minutes of birth was significantly associated with pathological CTG group compared to non-pathological CTG (p 0.05). Worsening of CTG from normal to pathological showed increasing rate of operative delivery. Even though sensitivity and positive predictive values of labour admission CTG were low, specificity and negative predictive values were high for detecting low Apgar score. Therefore, labour admission CTG has a value in excluding adverse perinatal outcomes in low-risk term pregnancies in spontaneous labour.展开更多
文摘Cardiotocography measures the fetal heart rate in the fetus during pregnancy to ensure physical health because cardiotocography gives data about fetal heart rate and uterine shrinkages which is very beneficial to detect whether the fetus is normal or suspect or pathologic.Various cardiotocography measures infer wrongly and give wrong predictions because of human error.The traditional way of reading the cardiotocography measures is the time taken and belongs to numerous human errors as well.Fetal condition is very important to measure at numerous stages and give proper medications to the fetus for its well-being.In the current period Machine learning(ML)is a well-known classification strategy used in the biomedical field on various issues because ML is very fast and gives appropriate results that are better than traditional results.ML techniques play a pivotal role in detecting fetal disease in its early stages.This research article uses Federated machine learning(FML)and ML techniques to classify the condition of the fetus.This study proposed a model for the detection of bio-signal cardiotocography that uses FML and ML techniques to train and test the data.So,the proposed model of FML used numerous data preprocessing techniques to overcome data deficiency and achieves 99.06%and 0.94%of prediction accuracy and misprediction rate,respectively,and parallel the proposed model applying K-nearest neighbor(KNN)and achieves 82.93%and 17.07%of prediction accuracy and misprediction accuracy,respectively.So,by comparing both models FML outperformed the KNN technique and achieved the best and most appropriate prediction results as compared with previous studies the proposed study achieves the best and most accurate results.
文摘Reducing neonatal mortality is a critical global health objective,especially in resource-constrained developing countries.This study employs machine learning(ML)techniques to predict fetal health status based on cardiotocography(CTG)examination findings,utilizing a dataset from the Kaggle repository due to the limited comprehensive healthcare data available in developing nations.Features such as baseline fetal heart rate,uterine contractions,and waveform characteristics were extracted using the RFE wrapper feature engineering technique and scaled with a standard scaler.Six ML models—Logistic Regression(LR),Decision Tree(DT),Random Forest(RF),Gradient Boosting(GB),Categorical Boosting(CB),and Extended Gradient Boosting(XGB)—are trained via cross-validation and evaluated using performance metrics.The developed models were trained via cross-validation and evaluated using ML performance metrics.Eight out of the 21 features selected by GB returned their maximum Matthews Correlation Coefficient(MCC)score of 0.6255,while CB,with 20 of the 21 features,returned the maximum and highest MCC score of 0.6321.The study demonstrated the ability of ML models to predict fetal health conditions from CTG exam results,facilitating early identification of high-risk pregnancies and enabling prompt treatment to prevent severe neonatal outcomes.
文摘Introduction: Labour admission cardiotocography (CTG) is commonly used non-invasive method of fetal monitoring in Sri Lanka. It may have a potentialto predict perinatal outcome in low-risk term pregnancies. Objectives: Objectives of the study were to determine the perinatal outcomes of normal, suspicious and pathological admission CTGs and role of labour admission cardiotocography as a predictive test for perinatal outcome in low-risk term pregnancies in spontaneous labour. Methods: This study was a prospective observational study done involving 445 low risk, term pregnancies in spontaneous labour. Labour admission CTG was performed in each pregnancy and categorized into normal, suspicious and pathological CTG according to criteria depicted by National Institute of Clinical Excellence (NICE) guideline 2007. Apgar score less than 7 at five minutes, resuscitation at birth, admission to neonatal intensive care unit (NICU), seizure within first 24 hours of birth and meconium-stained amniotic fluid were the primary outcome measures to assess fetal asphyxia. Mode of delivery in each category, nuchal cord at birth were also assessed. Results: Majority of participants were in 25-to-29-year age group and were nulliparous. Frequencies of normal, suspicious and pathological CTG were 74.8%, 18% and 7.2% respectively. Pathological CTG was significantly associated with low Apgar score compared to non-pathological CTG group (p 0.005) while other outcome measures were not significant. Rate of operative delivery was 68% in pathological group and 20.8% in non-pathological CTG group. Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of labour admission CTG to detect fetal asphyxia were 51.85%, 95.69%, 43.75% and 96.85% respectively. Conclusions: Incidence of pathological labour admission CTG was 7.2%. Apgar score less than 7 at five minutes of birth was significantly associated with pathological CTG group compared to non-pathological CTG (p 0.05). Worsening of CTG from normal to pathological showed increasing rate of operative delivery. Even though sensitivity and positive predictive values of labour admission CTG were low, specificity and negative predictive values were high for detecting low Apgar score. Therefore, labour admission CTG has a value in excluding adverse perinatal outcomes in low-risk term pregnancies in spontaneous labour.
