Study of Contributing Factors for Cure Response in Patients with AML.

      Аннотация: The outbreak of types of leukemia is on the rise and patients experience remarkably decreased life span as well as reduced quality of life along with their families. Moreover, treatment of this disease imposes heavy medical costs on the families of the patients and national healthcare systems. Selecting an appropriate therapy, therefore, is of the essence. Various chemotherapy regimens are employed to treat different types of leukemia and this makes it critical to conduct studies in order to identify the best type of therapy. This can be achieved by utilizing several parameters to evaluate the effectiveness of therapy and process of treatment of patients. Thanks to advances in computer science (Artificial Intelligence, bioinformatics, computational biology, etc.), the medical costs can be reduced to a great extent by taking advantage of such knowledge alongside laboratory experiments. This study intends to design a smart system that can use blood risk factors to determine the probability of mortality in a patient and ultimately, to select the best way of designing a system that provides probability of mortality using very simple blood data statistics.

Ключевые слова: risk factors, leukaemia, leukemia, fuzzy decision tree, Support vector machine (SVM), Artificial Neural Networks (ANNs), Acute myeloblastic leukemia (AML), Single perceptron neural network, Radial Basis Function (RBF) neural network