Abstract:Cardiotocography (CT G) is a simultaneous recording of foetal heart rate (F HR) and uterine contractions (UC) and it is one of the most common diagnostic techniques to evaluate maternal and foetal well-being during pregnancy and before delivery. Assessment of the foetal state can be verified only after delivery using the foetal (newborn) outcome data. One of the most important features defining the abnormal foetal outcome is low birth weight. This paper proposes a multi-class classification algorithm using Mod… Show more
“…Subject to the constraints (8) It is a well-known fact that SVM is meant for only classification into two classes [16]. By constructing and combining several binary classifiers, an n-class SVM can be designed.…”
Section: Multiclass Support Vector Machinesmentioning
confidence: 99%
“…Naïve Bayes Classifier has been used for classification of CTG data along with feature selection approaches in [7]. In [8], a classifier has been proposed which classifies the data into three classes by applying modular neural network. A neural network based classifier has been presented in [9], to improve the performance of clustering algorithms in CTG classification.…”
Abstract-Huge amount of data are available in the field of medicine which are used for diagnosing the diseases by analysing them. Presently, prediction of diseases are made easier and accurate by employing various data mining techniques to extract information from these medical data. This paper presents an improved method of classifying the cardiotocogram data using Multiclass Support Vector Machine (MSVM) through an optimized feature subset produced by Genetic Algorithm (GA). Various performance metrics have been evaluated and the experimental results exhibit improved performance when using optimized feature set comparing to the full feature set.
“…Subject to the constraints (8) It is a well-known fact that SVM is meant for only classification into two classes [16]. By constructing and combining several binary classifiers, an n-class SVM can be designed.…”
Section: Multiclass Support Vector Machinesmentioning
confidence: 99%
“…Naïve Bayes Classifier has been used for classification of CTG data along with feature selection approaches in [7]. In [8], a classifier has been proposed which classifies the data into three classes by applying modular neural network. A neural network based classifier has been presented in [9], to improve the performance of clustering algorithms in CTG classification.…”
Abstract-Huge amount of data are available in the field of medicine which are used for diagnosing the diseases by analysing them. Presently, prediction of diseases are made easier and accurate by employing various data mining techniques to extract information from these medical data. This paper presents an improved method of classifying the cardiotocogram data using Multiclass Support Vector Machine (MSVM) through an optimized feature subset produced by Genetic Algorithm (GA). Various performance metrics have been evaluated and the experimental results exhibit improved performance when using optimized feature set comparing to the full feature set.
“…An Artificial immune recognition system (AIRS) with fuzzy weighted preprocessing [18] is also used for arrhythmia classification. Multilayer perceptron model and Modular neural network model is applied in [19] and [20] respectively for multiclass ECG arrhythmia and fetal state classification problems. Various neural network models are used to classify ECG arrhythmia and classification accuracies are reported in [21][22][23][24].…”
Changes in the normal rhythm of a human heart may result in different cardiac arrhythmias, which may be immediately causes irreparable damage to the heart sustained over long periods of time. The ability to automatically identify arrhythmias from ECG recordings is important for clinical diagnosis and treatment. In this paper we proposed an Artificial Neural Network (ANN) based cardiac arrhythmia disease diagnosis system using standard 12 lead ECG signal recordings data. In this study, we are mainly interested in classifying disease in normal and abnormal classes. We have used UCI ECG signal data to train and test three different ANN models. In arrhythmia analysis, it is unavoidable that some attribute values of a person would be missing. Therefore we have replaced these missing attributes by closest column value of the concern class. ANN models are trained by static backpropagation algorithm with momentum learning rule to diagnose cardiac arrhythmia. The classification performance is evaluated using measures such as mean squared error (MSE), classification specificity, sensitivity, accuracy, receiver operating characteristics (ROC) and area under curve (AUC). Out of three different ANN models Multilayer perceptron ANN model have given very attractive classification results in terms of classification accuracy and sensitivity of 86.67% and 93.75% respectively while Modular ANN have given 93.1% classification specificity.
“…However, in the testing stage, a different set of data containing only input values is fed to the network. 23 Several configurations were studied through the application of the feed-forward back-propagation network in the modelling of the drying process. Selecting the input variables is one of the most important steps during the design and training phases of an ANN.…”
Methodology
ANN modelling theoryAn ANN is a data processing mathematical model. It consists of numerous units or elements called nodes or neurons. 19 These nodes or neurons are arranged in layers and are interconnected, with weights and biases between the layers. 15 The first layer is known as the input layer, and the last layer is known as the output layer. 20 The layers between the input and output layers are known as hidden layers. The number of input neurons depends on the num-
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