Bioinformatics / Biomedical Informatics / Medical Informatics / Health Informatics
Amin Janghorbani; Mohammad Hasan Moradi
Volume 10, Issue 3 , October 2016, , Pages 197-209
Abstract
Babies are born under 2,500 g., defined as low birth weight (LBW) babies. They are exposed to the higher risks of mortality, congenital malformations, mental retardation, and other physical and neurological impairments. 15.5 % of births around the world are LBW. Reduction of the rate of LBW births to ...
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Babies are born under 2,500 g., defined as low birth weight (LBW) babies. They are exposed to the higher risks of mortality, congenital malformations, mental retardation, and other physical and neurological impairments. 15.5 % of births around the world are LBW. Reduction of the rate of LBW births to one-third is one of the aims of United Nations Children’s Fund program. Prognosis of LBW births can play a critical role in the reduction of these cases. Also, it helps clinicians to make timely and efficient clinical decisions to save these babies' life. In this study, a hybrid framework called fuzzy evidential network with a good ability to manage different aspects of uncertainty is a selected as the LBW prognosis model. The accuracy of prognosis and the performance of the fuzzy evidential network in the management of missing values of the clinical database were investigated and compared with well-known prognosis models of LBW. The results showed that the fuzzy evidential network has higher prognosis accuracy (84.8%) than other prognosis models. On the other hand, the fusion of naïve Bayes and the fuzzy evidential network outputs resulted in higher prognosis accuracy (85.2%). In addition, the fuzzy evidential network performance in the management of uncertainty induced by imputation method, was better than other prognosis models of this study. The performance loss of this framework as the results of the missing data increment, is less than other models.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Amin Janghorbani; Mohammad Hasan Moradi; Abdollah Arasteh
Volume 7, Issue 2 , June 2013, , Pages 163-174
Abstract
Acute hypotension episodes (AHEs) are one of the hemodynamic instabilities with high mortality rate that is frequent among many groups of patients. Prognosis of acute hypotension episodes can help clinicians to diagnose the cause of this physiological disorder and select proper treatment based on this ...
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Acute hypotension episodes (AHEs) are one of the hemodynamic instabilities with high mortality rate that is frequent among many groups of patients. Prognosis of acute hypotension episodes can help clinicians to diagnose the cause of this physiological disorder and select proper treatment based on this diagnosis. In this study two groups of features, physiological and chaotic features, were extracted from the physiological time series to be applied for prediction of AHEs in the future 1 hour time interval. The best set of the features from the extracted features were selected using Genetic Algorithm (GA) and were classified by SVM. The prediction accuracy for physiological features was 87.5% and for chaotic features was 85%. In order to improve prediction accuracy, physiological and chaotic features were employed simultaneously in feature selection and the best combination of these features was selected by GA and classified by SVM. The best prognosis accuracy, which was achieved in this study by classification of the selected features, was 95% that was better than other previously studies on the same database.
