Prediction of intrapartum fetal hypoxia considering feature selection algorithms and machine learning models

Cömert, Zafer; Şengür, Abdulkadir; Budak, Ümit; Kocamaz, Adnan Fatih

doi:10.1007/s13755-019-0079-z

Cited by 46 publications

(25 citation statements)

References 44 publications

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“…The features are extracted from CTG signal recording. These effectively evaluate large amounts of real‐time data to provide better solutions and develop a framework for other models to perform classification (Cömert et al, 2019). CTC signals to directly assess the heart rate of the patients and give accurate results and updates to the medical experts.…”

Section: Related Workmentioning

confidence: 99%

Fetal health classification from cardiotocographic data using machine learning

et al. 2021

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Health complications during the gestation period have evolved as a global issue. These complications sometimes result in the mortality of the fetus, which is more prevalent in developing and underdeveloped countries. The genesis of machine learning (ML) algorithms in the healthcare domain have brought remarkable progress in disease diagnosis, treatment, and prognosis. This research deploys various ML algorithms to predict fetal health from the cardiotocographic (CTG) data by labelling the health state into normal, needs guarantee, and pathology. This work assesses the influence of various factors measured through CTG to predict the health state of the fetus through algorithms like support vector machine, random forest (RF), multi‐layer perceptron, and K‐nearest neighbours. In addition to this, the regression analysis and correlation analysis revealed the influence of the attributes on fetal health. The results of the algorithms show that RF performs better than its peers in terms of accuracy, precision, recall, F1‐score, and support. This work can further enhance more promising results by performing suitable feature engineering in the CTG data.

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Section: Related Workmentioning

confidence: 99%

Fetal health classification from cardiotocographic data using machine learning

et al. 2021

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“…Such breakthroughs have been especially seen in big data biological information processing [ 21 ] and big data information mining [ 22 ]. A doctor can predict a human's birth [ 23 ] and death [ 24 ] from physiological indicators through screening by feature selection technology. For drug development, feature selection and classifiers are used to predict functional classes of newly generated protein sequences [ 25 ] and protein inhibitors and substrates [ 26 ].…”

Section: Related Workmentioning

confidence: 99%

Personalized Body Constitution Inquiry Based on Machine Learning

Fan

Wen

et al. 2020

Journal of Healthcare Engineering

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Background. Body constitution (BC) is the abstract concept indicating the state of a person’s health in Traditional Chinese Medicine (TCM). The doctor identifies the body constitution of the patient through inspection and inquiry. Previous research simulates doctors to identify BC types according to a patient’s objective physical indicators. However, the lack of subjective feeling information can reduce the accuracy of the machine to imitate the doctor’s diagnosis. The Constitution in Chinese Medicine Questionnaire (CCMQ) is used to collect subjective information but suffers from low acquisition efficiency. Methods. This paper presents a personalized body constitution inquiry method based on a machine learning technique. It employs a random generator, a feature extractor, and a classifier to simulate the doctor inquiry and generate a personalized questionnaire. Specifically, the feature extractor evaluates and sorts the question of the constitution in the CCMQ based on the recognition results of the tongue coating image of patients. The sorted questions and relevant BC label are inputted into the classifier; the best questions are screened out for patients. Results. The experimental results show that our method can select personalized questions from the CCMQ for the patients, significantly reducing the time and the number of questions to answer. It also improves the accuracy of recognizing BC. Compared with the CCMQ, patients had 68.3% fewer questions to answer and the time occupied by answering is reduced by 80.3%. Conclusions. The proposed method can simulate the doctor's inquiry and pick out personalized questions for patients. It can act as auxiliary diagnosis tools to collect subjective patient feelings and help make further judgments on the patient’s BC types.

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“…Moreover, some authors also include in the definition the value of the base excess or base deficit (127). Some authors defined as "at risk of acidemia" when pH < 7.20 (33,47,(128)(129)(130)(131)(132)(133)(134)(135)(136) or pH < 7.15 (30,121,(137)(138)(139)(140)(141)(142)(143); others define when pH < 7.1 (43,126,(144)(145)(146) or even when pH < 7.05 (38,44,48,78,(147)(148)(149)(150)(151)(152)(153)(154)(155)(156)(157)(158)(159)(160)(161)(162)(163)(164)(165)…”

Section: Resultsmentioning

confidence: 99%

“…Moreover, some authors also include in the definition the value of the base excess or base deficit ( 127 ). Some authors defined as “at risk of acidemia” when pH < 7.20 ( 33 , 47 , 128 – 136 ) or pH < 7.15 ( 30 , 121 , 137 – 143 ); others define when pH < 7.1 ( 43 , 126 , 144 – 146 ) or even when pH < 7.05 ( 38 , 44 , 48 , 78 , 147 – 165 ). Some studies used clinical experts to identify episodes of hypoxia and asphyxia, such as, ( 54 , 166 , 167 ).…”

Section: Resultsmentioning

confidence: 99%

“…The number of articles probing the use of non-linear measures to assess the fHR signals analysis has been growing in the past decade. Non-linear analysis has been successfully applied in the study of fetal heart rate with several research objectives, such as fetal maturation or gestational age (197)(198)(199), fetal gender (182,200), labor stages (201,202), cesarean section (51,203), preterm birth (80,204), impact of nuchal cord on antenatal (205), fHR baseline (206), behavioral state (207,208), IUGR (209)(210)(211)(212)(213), hypoxia (128,137,214,215), fetal distress (216)(217)(218), maternal pathologies (219,220), and signal processing (198,(221)(222)(223)(224). Therefore, it is important that the scientific community is aware of the non-linear methods used depending on the research objective.…”

Section: Discussionmentioning

confidence: 99%

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Non-linear Methods Predominant in Fetal Heart Rate Analysis: A Systematic Review

et al. 2021

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The analysis of fetal heart rate variability has served as a scientific and diagnostic tool to quantify cardiac activity fluctuations, being good indicators of fetal well-being. Many mathematical analyses were proposed to evaluate fetal heart rate variability. We focused on non-linear analysis based on concepts of chaos, fractality, and complexity: entropies, compression, fractal analysis, and wavelets. These methods have been successfully applied in the signal processing phase and increase knowledge about cardiovascular dynamics in healthy and pathological fetuses. This review summarizes those methods and investigates how non-linear measures are related to each paper's research objectives. Of the 388 articles obtained in the PubMed/Medline database and of the 421 articles in the Web of Science database, 270 articles were included in the review after all exclusion criteria were applied. While approximate entropy is the most used method in classification papers, in signal processing, the most used non-linear method was Daubechies wavelets. The top five primary research objectives covered by the selected papers were detection of signal processing, hypoxia, maturation or gestational age, intrauterine growth restriction, and fetal distress. This review shows that non-linear indices can be used to assess numerous prenatal conditions. However, they are not yet applied in clinical practice due to some critical concerns. Some studies show that the combination of several linear and non-linear indices would be ideal for improving the analysis of the fetus's well-being. Future studies should narrow the research question so a meta-analysis could be performed, probing the indices' performance.

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Prediction of intrapartum fetal hypoxia considering feature selection algorithms and machine learning models

Cited by 46 publications

References 44 publications

Fetal health classification from cardiotocographic data using machine learning

Fetal health classification from cardiotocographic data using machine learning

Personalized Body Constitution Inquiry Based on Machine Learning

Non-linear Methods Predominant in Fetal Heart Rate Analysis: A Systematic Review

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