Development A Hybrid Model Based on Harris Hawks Optimization (HHO) Algorithm and Quantum Learning to Diagnosis of Prostate Cancer

Melisa Rahebi

Makale detayları

Development A Hybrid Model Based on Harris Hawks Optimization (HHO) Algorithm and Quantum Learning to Diagnosis of Prostate Cancer

Yazarlar:

Melisa Rahebi

Yayıncı:

Gazi University

Ana Başlıklar:

Bağlam Öğrenimi

Dergi:

Politeknik Dergisi Vol.28 Issue 3, pp.1063-1072

Belge Tipi:

Makale

Yayın Tarihi:

6/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001350508

pISSN:

2147-9429

Anahtar kelimeler:

Özet:

 Prostate cancer PC represents a significant health problem and stands among the primary mortality causes in men, partly due to the drawbacks of the diagnostic techniques currently used. Annually, these current diagnosis techniques cause many men to lose their lives simply because they cannot get an accurate diagnosis on time. A potentially practical and cost-effective approach for diagnosing PC is applying artificial intelligence, particularly machine learning. This work is aimed at developing a machine learning ML model for the diagnosis of prostate cancer on clinical data of 100 men, among whom 38 were suffering and 62 were healthy. The proposed model combines the Harris Hawk Optimization HHO and Quantum Learning QL methods. The results reveal that this new approach provides better accuracy, 97.84, than other ML approaches.

 Prostate cancer PC represents a significant health problem and stands among the primary mortality causes in men, partly due to the drawbacks of the diagnostic techniques currently used. Annually, these current diagnosis techniques cause many men to lose their lives simply because they cannot get an accurate diagnosis on time. A potentially practical and cost-effective approach for diagnosing PC is applying artificial intelligence, particularly machine learning. This work is aimed at developing a machine learning ML model for the diagnosis of prostate cancer on clinical data of 100 men, among whom 38 were suffering and 62 were healthy. The proposed model combines the Harris Hawk Optimization HHO and Quantum Learning QL methods. The results reveal that this new approach provides better accuracy, 97.84, than other ML approaches.

 Prostate cancer PC represents a significant health problem and stands among the primary mortality causes in men, partly due to the drawbacks of the diagnostic techniques currently used. Annually, these current diagnosis techniques cause many men to lose their lives simply because they cannot get an accurate diagnosis on time. A potentially practical and cost-effective approach for diagnosing PC is applying artificial intelligence, particularly machine learning. This work is aimed at developing a machine learning ML model for the diagnosis of prostate cancer on clinical data of 100 men, among whom 38 were suffering and 62 were healthy. The proposed model combines the Harris Hawk Optimization HHO and Quantum Learning QL methods. The results reveal that this new approach provides better accuracy, 97.84, than other ML approaches.

 Prostat kanseri PK önemli bir sağlık sorunudur ve kısmen kullanılan tanı tekniklerinin dezavantajları nedeniyle erkeklerde başlıca ölüm nedenleri arasında yer almaktadır. Her yıl, bu mevcut teşhis teknikleri, sadece zamanında doğru bir teşhis alamadıkları için birçok erkeğin hayatını kaybetmesine neden olmaktadır. PK teşhisi için potansiyel olarak pratik ve uygun maliyetli bir yaklaşım, yapay zeka, özellikle de makine öğrenimi uygulamaktır. Bu çalışma, 38i hasta ve 62si sağlıklı olan 100 erkeğin klinik verileri üzerinde prostat kanseri teşhisi için bir makine öğrenimi ML modeli geliştirmeyi amaçlamaktadır. Önerilen model Harris Hawk Optimizasyonu HHO ve Kuantum Öğrenme QL yöntemlerini birleştirmektedir. Sonuçlar, bu yeni yaklaşımın diğer ML yaklaşımlarına göre 97,84 oranında daha iyi doğruluk sağladığını ortaya koymaktadır.

Makale detayları

Development A Hybrid Model Based on Harris Hawks Optimization (HHO) Algorithm and Quantum Learning to Diagnosis of Prostate Cancer

Yazarlar:

Melisa Rahebi

Yayıncı:

Gazi University

Ana Başlıklar:

Bağlam Öğrenimi

Dergi:

Politeknik Dergisi Vol.28 Issue 3, pp.1063-1072

Belge Tipi:

Makale

Yayın Tarihi:

6/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001350508

pISSN:

2147-9429

Prostate cancer detection

Machine Learning

Cancer detection

Quantum learning

Harris Hawk Optimization (HHO)Prostat kanseri tespiti

Makine Öğrenimi

Kanser tespiti

Kuantum öğrenme

Harris Hawk Optimizasyonu (HHO)

Sayı

Makale detayları

Yazarlar: Melisa Rahebi

Yayıncı: Gazi University

Ana Başlıklar: Bağlam Öğrenimi

Dergi: Politeknik Dergisi Vol.28 Issue 3, pp.1063-1072

Belge Tipi: Makale

Yayın Tarihi: 6/2025

Belge Dili: İngilizce

Document ID (Org No.): 2001350508

pISSN: 2147-9429

Sayı

Yazarlar:

Melisa Rahebi

Yayıncı:

Gazi University

Ana Başlıklar:

Bağlam Öğrenimi

Dergi:

Politeknik Dergisi Vol.28 Issue 3, pp.1063-1072

Belge Tipi:

Makale

Yayın Tarihi:

6/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001350508

pISSN:

2147-9429