Bioelectrics
Zahra Sadat Hosseini; Seyed Mohammad Reza Hashemi Golpayegani
Volume 13, Issue 1 , April 2019, , Pages 69-84
Abstract
The esophageal carcinoma is the eight most predominate malignancy in the world and the sixth deadliest cancer. 80% of esophageal cancers occur in squamous cells. In Iran, this type of cancer is more prevalent in Golestan province. Before the onset of this type of cancer, histological precursor lesions ...
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The esophageal carcinoma is the eight most predominate malignancy in the world and the sixth deadliest cancer. 80% of esophageal cancers occur in squamous cells. In Iran, this type of cancer is more prevalent in Golestan province. Before the onset of this type of cancer, histological precursor lesions emerge in the epithelial tissue of esophageal mucosa that their progression and penetration into the underlying layers of epithelium lead to cancer. This disease starts from a pre-clinical phase in most patients. In most cases, the disease progresses to the same clinical stage in the absence of appropriate therapeutic interventions. In the literature of this cancer, there is no model for the progression of these lesions (dysplasia) at the mesoscopic level. In this study, by using microscopic images of normal and low-grade dysplasia biopsy samples, we proposed a dynamical model based on the globally coupled logistic maps. The model was designed and its parameters were set based on the assumptions of the esophageal epithelium structure, functionality and using the information about the fractal geometry of this tissue. The model performance was evaluated by computation the pattern of Lyapunov exponent variations across the epithelium thickness. In this model, the decreasing trend of this index for normal tissue had a reasonable accuracy and sensitivity to diagnose it from the low-grade dysplasia. Besides, the model results show that it can be a direct relationship between the structural complexity of this biological system and its timeliness uncertainty.
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.
