title: A variable structure fuzzy neural network model of squamous dysplasia and esophageal squamous cell carcinoma based on a global chaotic optimization algorithm.
abstract: Identification of squamous dysplasia and esophageal squamous cell carcinoma (ESCC) is of great importance in prevention of cancer incidence. Computer aided algorithms can be very useful for identification of people with higher risks of squamous dysplasia, and ESCC. Such method can limit the clinical screenings to people with higher risks. Different regression methods have been used to predict ESCC and dysplasia. In this paper, a Fuzzy Neural Network (FNN) model is selected for ESCC and dysplasia prediction. The inputs to the classifier are the risk factors. Since the relation between risk factors in the tumor system has a complex nonlinear behavior, in comparison to most of ordinary data, the cost function of its model can have more local optimums. Thus the need for global optimization methods is more highlighted. The proposed method in this paper is a Chaotic Optimization Algorithm (COA) proceeding by the common Error Back Propagation (EBP) local method. Since the model has many parameters, we use a strategy to reduce the dependency among parameters caused by the chaotic series generator. This dependency was not considered in the previous COA methods. The algorithm is compared with logistic regression model as the latest successful methods of ESCC and dysplasia prediction. The results represent a more precise prediction with less mean and variance of error.
authors.affiliation: Complex Systems and Cybernetic Control Lab., Faculty of Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, P.O. Box 1591634311, Iran.
authors.forename: Motahareh
authors.initials: M
authors.lastname: Moghtadaei
authors.forename: Mohammad Reza
authors.initials: MR
authors.lastname: Hashemi Golpayegani
authors.forename: Reza
authors.initials: R
authors.lastname: Malekzadeh
mesh_terms: D000465:Algorithms; D002294:Carcinoma, Squamous Cell / Q000175:diagnosis / Q000209:etiology; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D055088:Early Detection of Cancer / Q000379:methods; D004938:Esophageal Neoplasms / Q000175:diagnosis* / Q000209:etiology; D000077277:Esophageal Squamous Cell Carcinoma; D017143:Fuzzy Logic; D006801:Humans; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D011230:Precancerous Conditions / Q000175:diagnosis* / Q000209:etiology; D012307:Risk Factors