- Michał Jasiński, PhDFaculty of Electrical Engineering, Wroclaw University of Science and Technology, Wroclaw, PolandInterests: machine learning; deep learning; data mining; cancer prognosis
Cancer is recognized globally as an important public health problem. Unfortunately, gynecological cancers are also one of the main causes of female mortality in the world. Early identification of the prognosis of this disease is an important requirement in gynecological cancer care, as it can improve the subsequent clinical management of patients. Machine learning (ML) methods can be applied to support the prognostication of gynecologic cancer. The aim of this special issue is to present recent research on the development of predictive models that lead to more effective and accurate decision making. This can include work relating to the application of:
• Artificial Neural Networks (ANNs) for gynecologic cancer prognosis
• Bayesian Networks (BNs) for gynecologic cancer prognosis
• Support Vector Machines (SVMs) for gynecologic cancer prognosis
• Decision Trees (DTs) for gynecologic cancer prognosis
Dr. Michał Jasiński
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