Diagnosing the major contributing factors in the classification of the fetal health status using cardiotocography measurements: An AutoML and XAI approach

Dwivedi, Prakriti; Khan, Akbar Ali; Mugde, Sareeta; Sharma, Garima

doi:10.1109/ecai52376.2021.9515033

Cited by 11 publications

(8 citation statements)

References 8 publications

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“…XAI was used for understanding the classification of cardiotocography for fetal health status [256]. Light Gradient Boosting Machine algorithm was used for fetus health classification [256]. The dataset from Kaggle comprised of three classes and 21 features [256].…”

Section: Clinical/observational Datamentioning

confidence: 99%

“…Light Gradient Boosting Machine algorithm was used for fetus health classification [256]. The dataset from Kaggle comprised of three classes and 21 features [256]. SHAP was used for the data explainability [256].…”

Section: Clinical/observational Datamentioning

confidence: 99%

“…The dataset from Kaggle comprised of three classes and 21 features [256]. SHAP was used for the data explainability [256].…”

Section: Clinical/observational Datamentioning

confidence: 99%

See 2 more Smart Citations

Survey of explainable artificial intelligence techniques for biomedical imaging with deep neural networks

Nazir¹,

Dickson²,

Akram³

2023

Computers in Biology and Medicine

101

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Section: Clinical/observational Datamentioning

confidence: 99%

Section: Clinical/observational Datamentioning

confidence: 99%

See 1 more Smart Citation

Survey of explainable artificial intelligence techniques for biomedical imaging with deep neural networks

Nazir¹,

Dickson²,

Akram³

2023

Computers in Biology and Medicine

101

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“…LGBM algorithm to classify fetal health (50). They stated that the dataset they used was categorised as N, S and P and consisted of 2126 samples with 21 features.…”

Section: Dwivedi Et Al Proposed Thementioning

confidence: 99%

Artificial Intelligence in Fetal Health Diagnosis: A Systematic Literature Review

Kuzu,

Santur

2023

TUSEB

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Obstetricians commonly use Non-Stress Test to evaluate fetal well-being during the antepartum and intrapartum periods by measuring fetal heart rate and uterine contractions of the mother. Non-Stress Test is also used to diagnose fetal distress at an early stage. Early diagnosis and treatment can increase the fetus's survival rate and improve their quality of life. The fetal heart rate and uterine contraction signals obtained from the Non-Stress Test are recorded on a paper called a trace. Obstetricians interpret the trace to make decisions about the fetus's condition. However, traditional analysis of Non-Stress Test takes time, and there are differences in interpretation among experts. Newly qualified doctors and midwives are more prone to making mistakes and incorrect decisions. To overcome the differences in the interpretation of Non-Stress Test analysis and to automate the process to minimize diagnostic errors, machine learning and deep learning models have been increasingly used in recent years. In this study, the literature of the past five years is researched, and numerical expressions, tables, and graphs related to this are presented.

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“…Using k best selection and f_classif as score function as shown in Figure 4, we visualize the result by seaborn library using bar chart [8].…”

Section: Feature Selectionmentioning

confidence: 99%

Performance Comparison of Three Classifiers for Fetal Health Classification Based on Cardiotocographic Data

Khare¹,

Kumari²

2022

ATAIML

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The global child mortality rate, which is steadily declining, will be around 26 fatalities per 1000 live births in 2022. Numerous Sustainable Development Goals of the United Nations take into account the declining child mortality rate, which illustrates how far humanity has come. Cardiotocograms (CTGs) are a simple and affordable tool that most professionals choose to reduce infant and mother mortality. Three of the most cutting-edge methodologies are utilized in this research to classify the data, and their results are compared. All three classifiers outperformed the random forest, whose accuracy was 94.3%.

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Diagnosing the major contributing factors in the classification of the fetal health status using cardiotocography measurements: An AutoML and XAI approach

Cited by 11 publications

References 8 publications

Survey of explainable artificial intelligence techniques for biomedical imaging with deep neural networks

Survey of explainable artificial intelligence techniques for biomedical imaging with deep neural networks

Artificial Intelligence in Fetal Health Diagnosis: A Systematic Literature Review

Performance Comparison of Three Classifiers for Fetal Health Classification Based on Cardiotocographic Data

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