Michał Jasiński, PhD

Faculty of Electrical Engineering, Wroclaw University of Science and Technology, Wroclaw, Poland

Interests: machine learning; deep learning; data mining; cancer prognosis

Special Issue Information

Dear Colleagues,

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

Guest Editor

Keywords

cancers

gynecologic cancer prognosis

machine learning (ML)

artificial neural networks (ANNs)

bayesian networks (BNs)

support vector machines (SVMs)

decision trees (DTs)

Manuscript Submission Information

Manuscripts should be submitted via our online editorial system at https://imr.propub.com by registering and logging in to this website. Once you are registered, click here to start your submission. Manuscripts can be submitted now or up until the deadline. All papers will go through peer-review process. Accepted papers will be published in the journal (as soon as accepted) and meanwhile listed together on the special issue website. Research articles, reviews as well as short communications are preferred. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office to announce on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts will be thoroughly refereed through a double-blind peer-review process. Please visit the Instruction for Authors page before submitting a manuscript. The Article Processing Charge (APC) in this open access journal is 1500 USD. Submitted manuscripts should be well formatted in good English.

Clin. Exp. Obstet. Gynecol. Print ISSN 0390-6663 Electronic ISSN 2709-0094