Evaluating Prostate Cancer Diagnosis Using the Adaptive Neural Fuzzy Inference System (ANFIS): A Comparative Analysis of Diagnostic Accuracy

Orhan Dalkılıç; Naime Demirtaş; Abdullah Demirtaş

Makale detayları

Evaluating Prostate Cancer Diagnosis Using the Adaptive Neural Fuzzy Inference System (ANFIS): A Comparative Analysis of Diagnostic Accuracy

Yazarlar:

Orhan Dalkılıç | Naime Demirtaş | Abdullah Demirtaş

Yayıncı:

Fırat University

Ana Başlıklar:

Soft Computing

Dergi:

Turkish Journal of Science and Technology Vol.20 Issue 2, pp.583-593

Belge Tipi:

Makale

Yayın Tarihi:

9/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001357971

pISSN:

1308-9080

Anahtar kelimeler:

Özet:

 This study explores the application of the Adaptive Neural Fuzzy Inference System ANFIS in evaluating prostate cancer diagnosis outcomes. Prostate cancer remains one of the most prevalent cancers among men globally, where early and accurate detection is critical for effective treatment. Despite advancements, diagnosing prostate cancer is inherently complex due to the variability in clinical data and the need for precise interpretation. In this research, ANFISa hybrid methodology integrating fuzzy logic and neural networkswas employed to analyze a clinical dataset and develop a diagnostic model. The ANFIS framework excels in handling uncertainty and nonlinear relationships, making it particularly suited for medical diagnostics. The models performance was rigorously assessed using multiple evaluation metrics, including accuracy, sensitivity, and specificity. The results demonstrate that ANFIS achieves high diagnostic accuracy, significantly reducing unnecessary biopsies by 45.45 compared to traditional methods. This highlights its potential as a reliable decision-support tool in clinical settings. By leveraging ANFIS, clinicians can enhance diagnostic precision, optimize resource allocation, and improve patient outcomes. The study underscores the transformative role of intelligent systems in advancing prostate cancer management.

 This study explores the application of the Adaptive Neural Fuzzy Inference System ANFIS in evaluating prostate cancer diagnosis outcomes. Prostate cancer remains one of the most prevalent cancers among men globally, where early and accurate detection is critical for effective treatment. Despite advancements, diagnosing prostate cancer is inherently complex due to the variability in clinical data and the need for precise interpretation. In this research, ANFISa hybrid methodology integrating fuzzy logic and neural networkswas employed to analyze a clinical dataset and develop a diagnostic model. The ANFIS framework excels in handling uncertainty and nonlinear relationships, making it particularly suited for medical diagnostics. The models performance was rigorously assessed using multiple evaluation metrics, including accuracy, sensitivity, and specificity. The results demonstrate that ANFIS achieves high diagnostic accuracy, significantly reducing unnecessary biopsies by 45.45 compared to traditional methods. This highlights its potential as a reliable decision-support tool in clinical settings. By leveraging ANFIS, clinicians can enhance diagnostic precision, optimize resource allocation, and improve patient outcomes. The study underscores the transformative role of intelligent systems in advancing prostate cancer management.

 This study explores the application of the Adaptive Neural Fuzzy Inference System ANFIS in evaluating prostate cancer diagnosis outcomes. Prostate cancer remains one of the most prevalent cancers among men globally, where early and accurate detection is critical for effective treatment. Despite advancements, diagnosing prostate cancer is inherently complex due to the variability in clinical data and the need for precise interpretation. In this research, ANFISa hybrid methodology integrating fuzzy logic and neural networkswas employed to analyze a clinical dataset and develop a diagnostic model. The ANFIS framework excels in handling uncertainty and nonlinear relationships, making it particularly suited for medical diagnostics. The models performance was rigorously assessed using multiple evaluation metrics, including accuracy, sensitivity, and specificity. The results demonstrate that ANFIS achieves high diagnostic accuracy, significantly reducing unnecessary biopsies by 45.45 compared to traditional methods. This highlights its potential as a reliable decision-support tool in clinical settings. By leveraging ANFIS, clinicians can enhance diagnostic precision, optimize resource allocation, and improve patient outcomes. The study underscores the transformative role of intelligent systems in advancing prostate cancer management.

 Bu çalışma, prostat kanseri teşhis sonuçlarını değerlendirmede Adaptive Neural Fuzzy Inference Systemin ANFIS uygulamasını araştırmaktadır. Prostat kanseri, erken ve doğru tespitin etkili tedavi için kritik öneme sahip olduğu, küresel olarak erkekler arasında en yaygın kanserlerden biri olmaya devam etmektedir. İlerlemelere rağmen, prostat kanseri teşhisi klinik verilerdeki değişkenlik ve kesin yorumlama ihtiyacı nedeniyle doğası gereği karmaşıktır. Bu araştırmada, bulanık mantık ve sinir ağlarını entegre eden bir hibrit metodoloji olan ANFIS, bir klinik veri setini analiz etmek ve bir teşhis modeli geliştirmek için kullanılmıştır. ANFIS çerçevesi, belirsizlik ve doğrusal olmayan ilişkileri ele almada mükemmeldir ve bu da onu özellikle tıbbi teşhisler için uygun hale getirir. Modelin performansı, doğruluk, duyarlılık ve özgüllük dahil olmak üzere birden fazla değerlendirme metriği kullanılarak titizlikle değerlendirilmiştir. Sonuçlar, ANFISin yüksek teşhis doğruluğuna ulaştığını ve geleneksel yöntemlere kıyasla gereksiz biyopsileri 45,45 oranında önemli ölçüde azalttığını göstermektedir. Bu, klinik ortamlarda güvenilir bir karar destek aracı olarak potansiyelini vurgulamaktadır. ANFISten yararlanarak, klinisyenler tanısal hassasiyeti artırabilir, kaynak tahsisini optimize edebilir ve hasta sonuçlarını iyileştirebilir. Çalışma, prostat kanseri yönetimini ilerletmede akıllı sistemlerin dönüştürücü rolünü vurgulamaktadır.

Makale detayları

Evaluating Prostate Cancer Diagnosis Using the Adaptive Neural Fuzzy Inference System (ANFIS): A Comparative Analysis of Diagnostic Accuracy

Yazarlar:

Orhan Dalkılıç | Naime Demirtaş | Abdullah Demirtaş

Yayıncı:

Fırat University

Ana Başlıklar:

Soft Computing

Dergi:

Turkish Journal of Science and Technology Vol.20 Issue 2, pp.583-593

Belge Tipi:

Makale

Yayın Tarihi:

9/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001357971

pISSN:

1308-9080

Fuzzy logic

prostate cancer

ANFISBulanık mantık

prostat kanser

ANFIS

Sayı

Makale detayları

Evaluating Prostate Cancer Diagnosis Using the Adaptive Neural Fuzzy Inference System (ANFIS): A Comparative Analysis of Diagnostic Accuracy

Yazarlar: Orhan Dalkılıç | Naime Demirtaş | Abdullah Demirtaş

Yayıncı: Fırat University

Ana Başlıklar: Soft Computing

Dergi: Turkish Journal of Science and Technology Vol.20 Issue 2, pp.583-593

Belge Tipi: Makale

Yayın Tarihi: 9/2025

Belge Dili: İngilizce

Document ID (Org No.): 2001357971

pISSN: 1308-9080

Fuzzy logic

prostate cancer

ANFISBulanık mantık

prostat kanser

ANFIS

Sayı

Yazarlar:

Orhan Dalkılıç | Naime Demirtaş | Abdullah Demirtaş

Yayıncı:

Fırat University

Ana Başlıklar:

Soft Computing

Dergi:

Turkish Journal of Science and Technology Vol.20 Issue 2, pp.583-593

Belge Tipi:

Makale

Yayın Tarihi:

9/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001357971

pISSN:

1308-9080