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We use least squares support vector machine (LS-SVM) utilizing a binary decision tree for classification of cardiotocogram to determine the fetal state. The parameters of LS-SVM are optimized by particle swarm optimization. The robustness of the method is examined by running 10-fold cross-validation. The performance of the method is evaluated in te...
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Citations
... The notable research based on machine and deep learning methods for analyzing CTG signals is mentioned here. Using the least-squares support vector machine (LS-SVM)-based model and particle swarm optimization [14] classified CTG traces into normal, suspect, and pathological classes with 91.62% accuracy. However, additional helpful evaluation criteria, such as sensitivity and a specificity, must be added. ...
Cardiotocography (CTG) is considered the gold standard for monitoring fetal heart rate (FHR) during pregnancy and labor to estimate the danger of oxygen deprivation. Visual interpretation of CTG traces is complex and frequently results in high rates of false positives and false negatives, leading to unfavorable and unwanted outcomes such as fetal mortality or needless cesarean surgery. If the data are well-balanced, which is uncommon in medical datasets, machine learning techniques can be helpful in interpretation. This study is designed to determine classification performance under various data balance approaches. We propose a robust methodology for the automated extraction of features that use a deep learning model based on the one-dimensional convolutional neural network (1D-CNN). We used a public database containing 552 intrapartum CTG recordings. Due to the imbalance in the dataset, the experiments were conducted under a variety of conditions such as (i) an unbalanced dataset, (ii) undersampling, (iii) a weighted binary cross-entropy approach, and (iv) oversampling utilizing the synthetic minority oversampling technique (SMOTE). We found an excellent sensitivity (99.80% for the unbalanced dataset, 96.25% for the weighted binary cross-entropy approach, and 99.81% with SMOTE) except for the under sampling situation, in which the sensitivity was 85.71%. Moreover, the 1D-CNN model incorporating SMOTE yielded promising results in 88% specificity, 93.72% quality index (QI), and 95.10% area under the curve. The model exhibited excellent performance in terms of sensitivity in every scenario except for undersampling. The oversampling of training data with SMOTE yielded a decent level of specificity, demonstrating the model’s strong predictive capacity. In addition, the SMOTE scenario resulted in fewer training epochs, which is another accomplishment.
... The author of [11] introduced a novel clinical verdict support system built on an enhanced adaptive genetic algorithm and an extreme machine learning algorithm in [11], and the model's concluding classification accuracy reached 94 percent. The parameters used to detect the infantile state of an Electrocardiogram (ECG) were improved in [12] by utilizing the least squares support vector machine, swarm optimization, and a binary decision tree. ...
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.
... The work also establishes the fact that giving rules for identification is always better i.e DT even with lower accuracy is better interpretative for results rather than an Artificial neural network which resembles a black box where processes involved are unknown. Another work including all the features [31], focuses on studying fetal well-being using The Least Square SVM method with Particle Swarm Optimization and Decision Trees. This method yielded 91.62% accuracy with all 2162 instances and had been validated using 10-fold cross-validation. ...
The problem consists of underfitting of model while predicting heart disease through classification using Extreme learning machines (ELMs). ELMs do not require tuning, but can auto tune them based on various activation functions and weights. The Cardiotocography dataset was considered for the study with a 3-Class problem where the three stages of the foetus represented normal, suspect and pathological cases. The inbuilt activation function in Python gave an accuracy of 11.11%. Existing activation functions like sigmoid, hyperbolic tangent and Fourier were used to study the presence of heart disease in the foetus and the accuracy of classification of the disease was compared before and after feature selection. This problem occurs due to underfitting of the model.
... Yanweeii, et.al [2] ,Yadav. et.al [25] have accumulated a social event framework reliant on the reason behind parametric specifications by researching HRV (Heartbeat Changeability) from electrocardiogram and the data is earlier managed with coronary ailment surmise model is made that coordinates the coronary issue of a sick person. ...
These days, health-related diseases are increasing day by day due to lifestyle and genetics. Especially these days, heart disease is so common that people's lives are at risk. Blood pressure, cholesterol and pulse rate vary from person to person. However, according to proven clinical results, normal blood pressure is 90/120 and cholesterol is 129-100 mg/dL, Pulse 72, fasting blood glucose 100 mg/dL, heart rate 100-60 bpm, normal ECG, main vessel width 25 mm (1 inch) in the aorta only 8 μm in the capillaries. This article looks at the different classification techniques used to predict each person's risk level based on age and gender. Blood pressure, cholesterol, heart rate. A "disease prediction" system based on predictive modeling predicts a user's disease based on the symptoms the user enters into the system. The system analyzes the symptoms that the user provides as inputs and provides disease probabilities as outputs. Disease prediction is done by applying techniques like KNN, Decision tree classifiers, random forest algorithms, and more. This technique calculates the probability of a disease. Therefore, we obtain an average prediction accuracy probability of 86.48%.
... Our SVM AlexNet hybrid classification architecture resulted in faster convergence by avoiding weight recalculation in all layers. Contrary to our presented method where resources are only spent on determining the global gradient, leading reported architectures require intense time and space resources to compute local maxima [32][33][34][35][36][37][38]. ...
... Our SVM AlexNet hybrid classification architecture resulted in faster convergence by avoiding weight recalculation in all layers. Contrary to our presented method where resources are only spent on determining the global gradient, leading reported architectures require intense time and space resources to compute local maxima [32][33][34][35][36][37][38]. In AlexNet, the proportion of fully connected and convolution layers is more than 90% as compared to other algorithms. ...
... The intense computational nature of this model hindered its automation for fetus classification. SVMs provide reasonable accuracy but they are not preferred for large CTG datasets, as the complexity of the algorithm's training is a direct function of the dataset size [36]. DNNs [37] can be trained with a high-dimensional CTG dataset but excessive connections severely decrease computational efficiency, as reported previously [38]. ...
Artificial Intelligence is serving as an impetus in digital health, clinical support, and health informatics for an informed patient’s outcome. Previous studies deal only with classification accuracies of Cardiotocographpic (CTG) datasets and ignored computational time which is a relevant parameter in a clinical environment. This paper proposes a modified deep neural algorithm to classify rather untapped pathological and suspicious CTG recordings with desired time complexity. In our newly developed classification algorithm, AlexNet architecture is merged with Support Vector Machines (SVMs) at the fully connected layers to reduce time complexity. We used an open-source UCI (Machine Learning Repository) dataset of Cardiotocographpic (CTG) recordings. 2126 CTG recordings were divided into three classes (Normal, Pathological & Suspected) including 23 attributes that were dynamically programmed and fed to our algorithm. We employed a deep Transfer Learning (TL) mechanism to transfer pre-learned features to our model. To reduce time complexity, we implemented a strategy where layers in the convolutional base were partially trained to leave others in the frozen states. We used an ADAM optimizer for the optimization of hyper-parameters. The presented algorithm also outperforms the leading architectures (RCNNs, ResNet, DenseNet, and GoogleNet) in terms of real-time accuracy, sensitivity, and specificity of 99.72%, 96.67%, and 99.6% respectively, making it a viable candidate for clinical settings after real-time validation.
... Our SVM AlexNet hybrid classification architecture resulted in faster convergence by avoiding weight recalculation in all layers. Contrary to our presented method where resources are only spent on determining the global gradient, leading reported architectures require intense time and space resources to compute local maxima [32][33][34][35][36][37][38]. ...
... Our SVM AlexNet hybrid classification architecture resulted in faster convergence by avoiding weight recalculation in all layers. Contrary to our presented method where resources are only spent on determining the global gradient, leading reported architectures require intense time and space resources to compute local maxima [32][33][34][35][36][37][38]. In AlexNet, the proportion of fully connected and convolution layers is more than 90% as compared to other algorithms. ...
... The intense computational nature of this model hindered its automation for fetus classification. SVMs provide reasonable accuracy but they are not preferred for large CTG datasets, as the complexity of the algorithm's training is a direct function of the dataset size [36]. DNNs [37] can be trained with a high-dimensional CTG dataset but excessive connections severely decrease computational efficiency, as reported previously [38]. ...
Artificial intelligence is serving as an impetus in digital health, clinical support, and
health informatics for an informed patient’s outcome. Previous studies only consider classification
accuracies of cardiotocographic (CTG) datasets and disregard computational time, which is a relevant
parameter in a clinical environment. This paper proposes a modified deep neural algorithm to
classify untapped pathological and suspicious CTG recordings with the desired time complexity. In
our newly developed classification algorithm, AlexNet architecture is merged with support vector
machines (SVMs) at the fully connected layers to reduce time complexity. We used an open-source
UCI (Machine Learning Repository) dataset of cardiotocographic (CTG) recordings. We divided
2126 CTG recordings into 3 classes (Normal, Pathological, and Suspected), including 23 attributes that
were dynamically programmed and fed to our algorithm. We employed a deep transfer learning (TL)
mechanism to transfer prelearned features to our model. To reduce time complexity, we implemented
a strategy wherein layers in the convolutional base were partially trained to leave others in the
frozen states. We used an ADAM optimizer for the optimization of hyperparameters. The presented
algorithm also outperforms the leading architectures (RCNNs, ResNet, DenseNet, and GoogleNet)
with respect to real-time accuracies, sensitivities, and specificities of 99.72%, 96.67%, and 99.6%,
respectively, making it a viable candidate for clinical settings after real-time validation.
... is paper offers an FS stage to handle the large dimensional feature storage problem, in which a sample of important characteristics is picked to improve classification performance. In this research, a genetic algorithm is used with an LS-SVM classification [23]. GA is a multidimensional problem-solving technique that, similar to gradient search approaches, avoids the problem of local optima. ...
Bipolar disorder is marked by mood swings that alternate between mania and depression. The stages of bipolar disorder (BD), as one of the most common mental conditions, are often misdiagnosed as major depressive disorder (MDD), resulting in ineffective treatment and a poor prognosis. As a result, distinguishing MDD from BD at an earlier phase of the disease may aid in more efficient and targeted treatments. In this research, an enhanced ACO (IACO) technique biologically inspired by and following the required ant colony optimization (ACO) was utilized to minimize the number of features by deleting unrelated or redundant feature data. To distinguish MDD and BD individuals, the selected features were loaded into a support vector machine (SVM), a sophisticated mathematical technique for classification process, regression, functional estimates, and modeling operations. In respect of classifications efficiency and frequency of features extracted, the performance of the IACO method was linked to that of regular ACO, particle swarm optimization (PSO), and genetic algorithm (GA) techniques. The validation was performed using a nested cross-validation (CV) approach to produce nearly reliable estimates of classification error.
... In (Yılmaz and Kılıkçıer, 2013;Sindhu et al., 2015;Yılmaz, 2016), the determination of the fetal status based on CTG data is also modeled as a three classification problem. Yılmaz (2016) used three artificial neural network models, namely, the multi-layer perceptron neural network (MLPNN), probabilistic neural network (PNN), and generalized regression neural network (GRNN), to compare the evaluation of the fetal state and concluded that the PNN network model had the best overall classification effect. ...
... In Sindhu et al. (2015), the author proposed a new clinical decision support system based on the improved adaptive genetic algorithm (IAGA) and the Extreme Learning Machine (ELM) algorithm, and the final classification accuracy of the model reached 94%. In Yılmaz and Kılıkçıer (2013), based on the least squares support vector machine (LS-SVM), the particle swarm optimization (PSO) and the binary decision tree (BDT) were combined to optimize the parameters so as to determine the fetal status of the electrocardiogram. ...
Cardiotocography (CTG) recorded fetal heart rate and its temporal relationship with uterine contractions. CTG intelligent classification plays an important role in evaluating fetal health and protecting fetal normal growth and development throughout pregnancy. At the feature selection level, this study uses the Apriori algorithm to search frequent item sets for feature extraction. At the level of the classification model, the combination model of AdaBoost and random forest with the highest classification accuracy is finally selected by comparing various models. The suspicious class data in the CTG data set affect the overall classification accuracy. The number of suspicious class data is predicted by the multi-model ensemble method. Finally, the data set is fused from three classifications to two classifications. The classification accuracy is 0.976, and the AUC is 0.98, which significantly improves the classification effect. In conclusion, the method used in this study has high accuracy in model classification, which is helpful to improve the accuracy of fetal abnormality detection.
... In general, the models currently used in fetal monitoring, such as Neural Networks, Support Vector Machines, Decision Tree, Fuzzy algorithms, and Hybrid algorithms, have achieved accuracies of more than 90% for the identification of CTG data [1][2][3][4][5][6][7][8][9][10][11]. However, most of the existing CTG discriminant models are based on algorithms for balancing the sample distribution, which tend to regard this as normal distribution and ignore the problem of imbalanced CTG data, resulting in relatively low accuracy in identifying suspicious and pathologic classes, mostly range in 45%-84% and 66%-97% respectively. ...
... Classification of CTG signals with SVM alone as well as integrated with AdaBoost was described in (Zhang & Zhao, 2017). The authors of (Yilmaz & Kilikcier, 2013) applied the LS-SVM utilizing the binary decision tree and the particle swarm optimization. LS-SVM was also used in (Georgoulas et al., 2017;Comert, Kocamaz, & Subha, 2018), where various sets of features were investigated. ...
Objective: The work introduces ε-insensitive distance based approach to simplification (by reducing the rules number) of the fuzzy classifier rule base. To obtain premises of the initial rules, a modified clustering with pairs of ε-hyperballs procedure is used. The goal of the presented solutions is to achieve high quality support for fetal distress assessment, based on cardiotocographic (CTG) signals classification with a reduced number of fuzzy rules.
Methods: In the presented rule base simplification solution, two rules are considered similar (or contradictory) when the distances between their premises do not exceed the assumed value of ε. The proposed simplification process consists of two phases: the first with combining similar rules into representative rules, and the second (optional) with the removal of contradictory rules. In addition, two methods of determining conclusions for representative rules are considered: a representative rule retains the original (unchanged) conclusion, or from the conclusions of similar rules the one with the highest absolute value is chosen. In the introduced clustering with pairs of ε-hyperballs the sizes of object classes are taken into account, to reduce the potential adverse impact of the unbalanced classes in the considered research material. In experiments, two reference assessments for CTG signals based on the retrospective fetal state evaluation were considered.
Results and conclusions: In the two-stage classification, the modified clustering outperformed the original procedure in terms of classification sensitivity and the QI index being the geometric mean of sensitivity and specificity. Among the examined rule base simplification methods, we consider the best to be the one based only on combining similar rules, with the unchanged value of conclusion for a representative rule. With the smallest number of rules (after simplification), an increased sensitivity, and in the case of pH-based reference assessment also increased QI value is obtained. Moreover, the achieved sensitivity and QI are higher in comparison to the reference methods and values reported in literature.
Significance and main impact: The results confirmed the effectiveness of the ε-insensitive distance rule base simplification. The proposed methods can be applied to any fuzzy rules with premise membership functions with a defined center. Therefore, we believe that this work may have a positive impact on other studies concerning fuzzy rule-based systems.
