Scopus
YÖKSİS DOI Eşleşti
SJR Q1
Prognosis of prostate cancer by artificial neural networks
Expert Systems with Applications · Ocak 2010
YÖKSİS Kayıtları
Prognosis of prostate cancer by artificial neural networks
Expert Systems with Applications · 2010 SCI-Expanded 30 atıf
Prof. Dr. İSMAİL SARITAŞ →
Prognosis of prostate cancer by artificial neural networks
EXPERT SYSTEMS WITH APPLICATIONS · 2010 SCI-Expanded
Doç. Dr. İLKER ALİ ÖZKAN →
Prognosis of prostate cancer by artificial neural networks
Expert Systems with Applications · 2010 SCI-Expanded
Doç. Dr. İLKER ALİ ÖZKAN →
YÖKSİS Kayıtları — ISSN Eşleşmesi
Game-theoretic DEA Optimization for Sustainable Agricultural Carbon Trading: Evidence from Türkiye’s Maize Production
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Web based medical decision support system application of Coronary Heart Disease diagnosis with Boolean functions minimization method
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Modeling and prediction of surface roughness in turning operations using artificial neural network and multiple regression method
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A fuzzy clustering approach for finding similar documents using a novel similarity measure
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A new approach on search for similar documents with multiple categories using fuzzy clustering
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Organizational strategy development in distribution channel management using fuzzy AHP and hierarchical fuzzy TOPSIS
2012 ISSN: 09574174 SCI-Expanded
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Artificial neural network and fuzzy expert system comparison for prediction of performance and emission parameters on a gasoline engine
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Artificial neural network and fuzzy expert system comparison for prediction of performance and emission parameters on a gasoline engine
2011 ISSN: 0957-4174 SCI-Expanded 4 atıf
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Fuzzy expert system design for operating room air-condition control systems
2009 ISSN: 0957-4174 SCI-Expanded 29 atıf Q1
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Prognosis of prostate cancer by artificial neural networks
2010 ISSN: 0957-4174 SCI-Expanded 30 atıf Q1
Prof. Dr. İSMAİL SARITAŞ →
Prediction of diesel engine performance using biofuels with artificial neural network
2010 ISSN: 0957-4174 SCI-Expanded 53 atıf Q1
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The effects of fuzzy control of magnetic flux on magnetic filter performance and energy consumption
2010 ISSN: 0957-4174 SCI-Expanded 5 atıf Q1
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Artificial neural network and fuzzy expert system comparison for prediction of performance and emission parameters on a gasoline engine
2011 ISSN: 0957-4174 SCI-Expanded 24 atıf Q1
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Anesthetic gas control with neuro fuzzy system in anesthesia
2010 ISSN: 09574174 SCI-Expanded
Prof. Dr. RÜŞTÜ GÜNTÜRKÜN →
An Adaptive Network Based Fuzzy Inference System ANFIS for the prediction of stock market return The case of the Istanbul Stock Exchange
2010 ISSN: 09574174 SCI-Expanded
Prof. Dr. MELEK ACAR →
Predicting direction of stock price index movement using artificial neural networks and support vector machines The sample of the Istanbul Stock Exchange
2011 ISSN: 09574174 SCI-Expanded
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Predicting bank financial failures using neural networks support vector machines and multivariate statistical methods A comparative analysis in the sample of savings deposit insurance fund SDIF transferred banks in Turkey
2009 ISSN: 09574174 SCI-Expanded
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The design of ultrasonic therapy device via fuzzy logic
2011 ISSN: 09574174 SCI-Expanded
Öğr. Gör. SEMA YILDIRIM →
Assessment of exercise stress testing with artificial neural network in determining coronary artery disease and predicting lesion localization
2009 ISSN: 09574174 SCI-Expanded
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2008 ISSN: 0957-4174 SCI-Expanded 85 atıf Q1
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Makale Bilgileri
ISSN09574174
Yayın TarihiOcak 2010
Cilt / Sayfa37 · 6646-6650
Scopus ID2-s2.0-80053578933
Özet
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. © 2010 Elsevier Ltd. All rights reserved.
Yazarlar (3)
1
Ismail Saritas
2
Ilker Ali Ozkan
3
Unal Sert
Anahtar Kelimeler
Artificial neural network
Prognosis of prostate cancer
Prostate cancer
Prostate-specific antigen
Kurumlar
Selçuk Üniversitesi
Selçuklu Turkey
Scimago Dergi (ISSN Eşleşmesi)
Expert Systems with Applications
Q1
SJR Skoru1,854
H-Index290
YayıncıElsevier Ltd
ÜlkeUnited Kingdom
Artificial Intelligence (Q1)
Computer Science Applications (Q1)
Engineering (miscellaneous) (Q1)
Metrikler
52
Atıf
3
Yazar
4
Anahtar Kelime