Cardiotocography Class Status Prediction Using Machine Learning Techniques

Appaji, Sangapu Venkata; Shankar, Ravi; Murthy, K. V. S.; Rao, Chinta Someswara

doi:10.5958/0976-5506.2019.01961.2

Cited by 3 publications

(4 citation statements)

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“…After the feature selection, the models were applied and compared with the existing models. Hence it was proved that our proposed work had the best accuracy of 94% compared with 92% [7], 91% [10], 92.37% [11], 87.4% [12], 85% [14], 91.5% [15], 90.76% [18], 89.54% [19], 87% [22] and 91% [23]. The proposed work was done with a rare dataset, and the features were selected using PSP with KNN to predict the exact target value so that the patients would not suffer with these correct results.…”

Section: Discussionmentioning

confidence: 72%

“…The random forest approach also provides a brute-force parameter adjustment method that facilitates more accessible feature selection. The linear support vector machine (SVM) technique is used for linearly separable data, which means that a dataset is considered linearly separable if it can be separated into two groups with only a single straight line [7]- [9]. After that, the linear SVM classifier is applied to the data to classify it.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of cardiovascular disease in diabetic patients using machine learning techniques

Nrusimhadri,

Swain,

Rao

et al. 2024

IJPHS

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Machine learning (ML) improves operations in many industries, including medicine. It affects the prognosis of several disorders, including heart disease. If predicted, it may provide doctors with new insights and allow them to treat each patient individually. If anticipated, it may provide medical practitioners with valuable information. Our team uses machine learning algorithms to study heart disease risk. This research will compare decision trees, AdaBoost, support vector machines, artificial neural networks (ANN), and customized ANN. The study will include this analysis. The given model will leverage the dataset of general information and medical test results. Our model uses particle swarm optimization (PSO) and k-nearest neighbors (KNN). Algorithm for feature selection. The model reduces dimensionality using evolutionary algorithms and neural networks. We compared the numerous assessment criteria to the current models, our model, and earlier models. Because of this, the suggested model's suitability was rated with the highest accuracy.

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Section: Discussionmentioning

confidence: 72%

Section: Introductionmentioning

confidence: 99%

Evaluation of cardiovascular disease in diabetic patients using machine learning techniques

Nrusimhadri,

Swain,

Rao

et al. 2024

IJPHS

View full text Add to dashboard Cite

show abstract

“…Appaji Sangapu Venkata et al [9] proposed their work on classifying cardiotocography class status. They used ML algorithms based on uterine contraction (UC) data and fetal heart rate (FHT) signals.…”

Section: Related Workmentioning

confidence: 99%

A Novel Approach for Prediction of Gestational Diabetes based on Clinical Signs and Risk Factors

Reddy¹,

Gadiraju²,

Nanjundan³

et al. 2023

EAI Endorsed Scal Inf Syst

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Gestational diabetes mellitus occurs due to high glucose levels in the blood. Pregnant women are affected by this type of diabetes. A blood test is to be performed to identify diabetes. The Oral Glucose Tolerance Test (OGTT) is a blood test performed between the 24th and 28th week of pregnancy that is necessary to identify and overcome the side effects of GDM. The main objective of this work is to train a model by utilizing the training data, evaluate the trained model using the test data, and compare existing machine learning algorithms with a Gradient boosting machine (GBM) to achieve a better model for the effective prediction of gestational diabetes. In this work, the analysis was done with a few existing algorithms and the Extreme learning machine and Gradient boosting techniques. The k-fold cross-validation technique is applied with values of k as 3, 5, and 10 to obtain better performance. The existing algorithms implemented are the Naive Bayes classifier, Support Vector Machine, K-Nearest Neighbour, ID3, CART and J48. The proposed algorithms are Gradient boosting and ELM. These algorithms are implemented in R programming. The metrics like accuracy, kappa statistic, sensitivity/Recall, specificity, precision, f-measure and AUC are used to compare all the algorithms. GBM has obtained better performance than existing algorithms. Then finally, GBM is compared with the other proposed robust Machine Learning algorithm, namely the Extreme learning machine, and the GBM performed better. So, It is recommended to use a gradient-boosting algorithm to predict gestational diabetes effectively.

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“…Appaji et al used ML methods including DT, RF and adaptive boosting to classify a dataset consisting of 2126 samples with 23 features from the UCI database (77). They also visualised the information obtained, stating that this would help doctors to treat patients.…”

Section: Artificial Intelligence In Fetal Healthmentioning

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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Cardiotocography Class Status Prediction Using Machine Learning Techniques

Cited by 3 publications

References 0 publications

Evaluation of cardiovascular disease in diabetic patients using machine learning techniques

Evaluation of cardiovascular disease in diabetic patients using machine learning techniques

A Novel Approach for Prediction of Gestational Diabetes based on Clinical Signs and Risk Factors

Artificial Intelligence in Fetal Health Diagnosis: A Systematic Literature Review

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