Cardiotocography is a scientific term for observing and recording infant heart rate and uterine spasms throughout pregnancy to assess infant comfort and detect a boosted possibility of pregnancy complications. This enables the early diagnosis and treatment of fetal hypoxia before it progresses to unadorned asphyxia or death (Ingemarsson 2009). The fetus's heart rate and its volatility, reactivity, and potential decelerations during uterine spasms are important indicators of infant well-being (News 1997).
The author of (Sindhu et al. 2015) introduced a novel clinical verdict support system built on an enhanced adaptive genetic algorithm and an extreme machine learning algorithm in (Sindhu et al. 2015), and the model's concluding classification accuracy reached 94 percent. The parameters used to detect the infantile state of an ECG were improved in (Yilmaz et al. 2013) utilizing the least squares support vector machine, swarm optimization, and a binary decision tree.
The author of (Zeng et al. 2021) develops a time-frequency function-based classifier that includes a cost-sensitive SVM. The cardiotocography's non-stationarity and the data set's instability are corrected, and more effective findings are achieved, with a specificity of 66.1 percent, a sensitivity of 85.2 percent, and a qualitative scale of 75.0 percent.
(Importa et al. 2019) used self-developed cardiotocography autonomous analytic software to extract descriptive data from cardiotocography signals and forecast delivery using a variety of modes, including AdaBoosting, random forests, J48, and gradient boosting tree. With a prediction accuracy of 87.6 percent and an area under the curve of 93.0 percent, the random forest classification results were the best.
The authors of (Ricciardi et al. 2020) used bespoke software to collect seventeen existing cardiotocography data parameters and classified them utilizing three machine learning algorithms i-e random forest, J48, and AdaBoosting decision tree, with random forest beating the others. The area under the curve for classification is more than 94.9 percent.
In (Amin et al. 2021), the author suggested and compared a backpropagation-based duration neutrophil performance neural network framework to other algorithms such as neural network, decision tree, KNN, and approximation neural network, demonstrating that this framework is a solution. useful and efficient.
Using primary module analysis, receiver functioning descriptions, and International Federation of Obstetrics and Gynecology guidelines, the author of (Jezewski et al. 2014) confirmed the impact of an application on delivery quality. The standard SisPorto data set and the Lagrange support vector machine were used to assess the infant's condition.
The authors contrasted 11 infant heart rate morphological analyses provided by the automated analysis approach with expert consensus (de l’Aulnoit et al. 2018). Conclude that the automatic analysis approach proposed by Lu and Wei outperformed previous automatic analysis methods in terms of baseline calculation.
In (Alam et al. 2022), the authors used numerous comparative machine learning algorithms such as logistic regression, random forest, decision tree, SVM, voting classifier, and KNN. Their model random forest achieved the best prediction accuracy of 97.51%.
In (Ogasawara et al. 2021), the authors employed a deep neural network to predict the fetal heart rate and uterine spasm with the help of numerous patterns and parameters. Their employed model achieved an area under the curve of 0.7 and a sensitivity of 89%.
In (Rahmayanti et al. 2022), the authors used a comparative machine learning approach to predict the fetal heart rate and uterine spasm and they employed five algorithms such as XG Boost, support vector machine, KNN, light GBM, and random forest. So, the random forest algorithm outperformed and achieved 98.00% prediction accuracy.
(Huang et al. 2012) proposes the use of arithmetical features derived from experiential modal rottenness. The characteristics derived from the breakdown of sub-bands are classed as normal or dangerous. They got 86 percent prediction accuracy of the test data right.
Table 1 shows the limitations of previous studies and it depicts that the (Sindhu et al. 2015) study used extreme machine learning with a publicly available clinical signal dataset and achieved 94% prediction accuracy with feature engineering and unbalanced prediction classes as research limitations, (Zeng et al. 2021) study used support vector machine with publicly available clinical signal dataset and achieved 82.5% sensitivity and 66.1% specificity with prediction accuracy and handcrafted features as research limitation, (Importa et al. 2019) study used ada boost, random forest, J48 and decision tree with publicly available clinical signal dataset and achieved 87.6% prediction accuracy and 93% AUC with unbalanced prediction classes as research limitation, (Ricciardi et al. 2020) study used random forest, J48 and ada boost with publicly available clinical signal dataset and achieved 94.9% AUC with prediction accuracy and handcrafted features as research limitation, (Alam et al. 2022) study used random forest, logistic regression, KNN and SVM with publicly available clinical signal dataset and achieved 97.51% prediction accuracy with feature engineering and unbalanced prediction classes as research limitation, (Ogasawara et al. 2021) study used deep neural network with publicly available clinical signal dataset and achieved 89% sensitivity and 0.7 AUC with unbalanced prediction classes as research limitation, (Rahmayanti et al. 2022) study used XG boost, SVM, KNN and light GBM with publicly available clinical signal dataset and achieved 98% prediction accuracy with unbalanced prediction classes as research limitation and (Huang et al. 2012) study used machine learning with publicly available clinical signal dataset and achieved 86% prediction accuracy with handcrafted features as research limitation.