Influence of Input Data Modification of Neural Networks Applied to the Fetal Outcome Classification
نویسندگان
چکیده
Cardiotocographic (CTG) fetal monitoring based on automated analysis of the fetal heart rate signal is widely used for fetal assessment. The high efficiency in diagnosis of cases with no fetal risk makes it a valuable screening method. However, the conclusion generation system is still needed to improve the fetal outcome prediction. Classification of the CTG records by means of neural networks is presented in this paper. Multi-layer perceptron neural networks were learned through 17 parameters obtained from computerized analysis of 749 traces from 103 patients, where 210 records related to abnormal fetal outcome. Classification efficiency was retrospectively verified by the real fetal outcome defined by newborn delivery data. Influence of numerical and categorical representation of the input variables, different data sets during learning, and gestational age as an additional information, were investigated in various experiments. The cases were fifty times randomly assigned to learning, validating and testing data sets. The best sensitivity and specificity were achieved for numerical input variables and with real proportion between normal and abnormal cases during learning. Key-Words: fetal heart rate monitoring, neural networks, pattern classification, signal analysis
منابع مشابه
Modelling of Conventional and Severe Shot Peening Influence on Properties of High Carbon Steel via Artificial Neural Network
Shot peening (SP), as one of the severe plastic deformation (SPD) methods is employed for surface modification of the engineering components by improving the metallurgical and mechanical properties. Furthermore artificial neural network (ANN) has been widely used in different science and engineering problems for predicting and optimizing in the last decade. In the present study, effects of conv...
متن کاملAn adaptive estimation method to predict thermal comfort indices man using car classification neural deep belief
Human thermal comfort and discomfort of many experimental and theoretical indices are calculated using the input data the indicator of climatic elements are such as wind speed, temperature, humidity, solar radiation, etc. The daily data of temperature، wind speed، relative humidity، and cloudiness between the years 1382-1392 were used. In the First step، Tmrt parameter was calculated in the Ray...
متن کاملOn the use of back propagation and radial basis function neural networks in surface roughness prediction
Various artificial neural networks types are examined and compared for the prediction of surface roughness in manufacturing technology. The aim of the study is to evaluate different kinds of neural networks and observe their performance and applicability on the same problem. More specifically, feed-forward artificial neural networks are trained with three different back propagation algorithms, ...
متن کاملOptimization of Oleuropein Extraction from Olive Leaves using Artificial Neural Network
In this work, the artificial neural networks (ANN) technology was applied to the simulation of oleuropein extraction process. For this technology, a 3-layer network structure is applied, and the operation factors such as amount of flow intensity ratio, temperature, residence time, and pH are used as input variables of the network, whereas the extraction yield is considere...
متن کاملPrediction of pore facies using GMDH-type neural networks: a case study from the South Pars gas field, Persian Gulf basin
The current study proposes a two-step approach for pore facies characterization in the carbonate reservoirs with an example from the Kangan and Dalanformations in the South Pars gas field. In the first step, pore facies were determined based on Mercury Injection Capillary Pressure (MICP) data incorporation with the Hierarchical Clustering Analysis (HCA) method. In the next step, polynomial meta...
متن کامل