Prognosis of prostate cancer by artificial neural networks

Authors:
Ismail Saritas;Ilker Ali Ozkan;Ibrahim Unal Sert
Affiliations:
Department of Electronic and Computer Education, Technical Education Faculty, Selcuk University, Konya, Turkey;Department of Electronic and Computer Education, Technical Education Faculty, Selcuk University, Konya, Turkey;Department of Urology, Meram Medicine Faculty, Selcuk University, Konya, Turkey
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

Citing 5
Cited 4

Survey of utilisation of fuzzy technology in medicine and healthcare

Fuzzy Sets and Systems - Special issue on clustering and learning
A fuzzy expert system design for diagnosis of prostate cancer

CompSysTech '03 Proceedings of the 4th international conference conference on Computer systems and technologies: e-Learning
Early prostate cancer diagnosis by using artificial neural networks and support vector machines

Expert Systems with Applications: An International Journal
A fuzzy logic based-method for prognostic decision making in breast and prostate cancers

IEEE Transactions on Information Technology in Biomedicine
Knowledge acquisition in the fuzzy knowledge representation framework of a medical consultation system

Artificial Intelligence in Medicine

Using the Taguchi method for effective market segmentation

Expert Systems with Applications: An International Journal
Prediction of Breast Cancer Using Artificial Neural Networks

Journal of Medical Systems
Fuzzy expert system for predicting pathological stage of prostate cancer

Expert Systems with Applications: An International Journal
A hybrid fuzzy-ontology based intelligent system to determine level of severity and treatment recommendation for Benign Prostatic Hyperplasia

Computer Methods and Programs in Biomedicine

Quantified Score

Hi-index	12.05

Visualization

Abstract

In this study, an artificial neural network has been devised that yields a prognostic result indicating whether patients have cancer or not using their free prostate-specific antigen, total prostate-specific antigen and age data. Though this system does not diagnose cancer conclusively, it helps the doctor in deciding whether a biopsy is necessary by providing information about whether the patient has prostate cancer or not. Data from 121 patients who were definitively diagnosed with cancer after biopsy were used in devising the system. The results of the definitive diagnoses of the patients and the results of the ANN that was performed were analysed using confusion matrix and ROC analyses. As a result of ANN, which was implemented on the basis of these analyses, success rates of 94.11% and 94.44% were achieved for prognosis of disease and validity, respectively. The ANN, which yielded these high rates of reliability, will help doctors make quick and reliable diagnoses without any risks and make it a better option to monitor patients with low prostate cancer risk on whom biopsies must not be carried out through a policy of wait and see.