The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON COMPUTERS
Transactions ID Number: 89-305
Full Name: Corina Botoca
Position: Associate Professor
Age: ON
Sex: Female
Address: Bd.V.Parvan, No.2, Timisoara, 300023
Country: ROMANIA
Tel: 403308
Tel prefix: +40256
Fax: 403259
E-mail address: corina.botoca@etc.upt.ro
Other E-mails: corinabotoca@yahoo.com
Title of the Paper: Prostate cancer prognosis evaluation assisted by neural networks
Authors as they appear in the Paper: Corina Botoca, Razvan Bardan, Mircea Botoca And Florin Alexa
Email addresses of all the authors: corina.botoca@etc.upt.ro, rbardan@yahoo.com, mbotoca@yahoo.com, florin.alexa@yahoo.com
Number of paper pages: 10
Abstract: Neural networks (NN) are new promising tools that can assist the clinicians in the diagnosis process and in therapy decision making, because they can deal with a great number of parameters, learning from examples and assessing any nonlinear relationships between inputs and outputs. In this paper, the problem of prostate cancer evolution prediction is approached using NN. The original database contained 650 records of patients, which underwent radical prostatectomy for prostate cancer. The NN variables were the parameters with the highest prognostic value selected and pre-processed from the original database. Different NN architectures and NN parameters have been tested in order to obtain the best complexity/accuracy ratio. The input data were structured, according to the latest statistical and representation concepts used in the current medical practice, aiming to improve the global performance. Different experiments were done using the rough database and the struc!
tured database. The NN performances were compared with the most widely used prediction statistical method, the logistic regresion. All NN models performed better than the logistic regression. The best obtained global prediction of correct classification 96.94% is better than the results of similar experiments available in literature. The NN prediction performance might be improved, because, in our opinion, its limits are given by the relatively small number of cases and the methods of collecting data.
Keywords: Neural networks, Prostate cancer, Prediction, Capsule penetration
EXTENSION of the file: .pdf
Special (Invited) Session: Prediction of prostate capsule penetration using neural networks
Organizer of the Session: 697-329
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