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Journals / Researcher / 2025 / Cilt: 05 Sayı: 01

A new multiobjective Harris Hawk Optimization algorithm for the diagnosis of breast cancer

Meme kanseri teşhisi için yeni bir çok amaçlı Harris Şahin Optimizasyonu algoritması

Alara Sermutlu(TED ÜNİVERSİTESİ)0009-0005-3479-4640
Tansel Dökeroğlu(TED ÜNİVERSİTESİ)0000-0003-1665-5928
Journal
Researcher
Pages
17–25
Publication Date
2025-07-31
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The proposed approach achieves 31-45% reductions in the total number of selected features across all datasets, significantly lowering computational costs and improving the accuracy of diagnostics up to 95-97%.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Breast cancer, a highly prevalent and life-threatening disease, affects millions of individuals worldwide, particularly women.
📊
Key Findings & Data:Feature-based methods are widely employed for early diagnosis of breast cancer, and selecting the optimal feature set remains a significant and challenging problem.
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Conclusion & Impact:The proposed approach achieves 31-45% reductions in the total number of selected features across all datasets, significantly lowering computational costs and improving the accuracy of diagnostics up to 95-97%.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:meme kanseri · Harris Şahini · meta-sezgizel

Abstract

Breast cancer, a highly prevalent and life-threatening disease, affects millions of individuals worldwide, particularly women. Feature-based methods are widely employed for early diagnosis of breast cancer, and selecting the optimal feature set remains a significant and challenging problem. In this study, we introduce a novel Multi-objective Harris Hawk Optimization algorithm, which integrates an adaptive K-Nearest Neighbor classifier. Comprehensive experiments were conducted on two well-known datasets. The proposed approach achieves 31-45% reductions in the total number of selected features across all datasets, significantly lowering computational costs and improving the accuracy of diagnostics up to 95-97%.

Özet

Oldukça yaygın ve ölümcül bir hastalık olan meme kanseri, dünya genelinde, milyonlarca kadını etkilemektedir. Meme kanseri erken teşhisi için özellik tabanlı yöntemler yaygın olarak kullanılmakta olup, en uygun özellik kümesini seçmek önemli ve zorlu bir problemdir. Bu çalışmada, uyarlamalı K-En Yakın Komşu sınıflandırıcısı entegre edilmiş yeni bir Çok Amaçlı Harris Şahin Optimizasyonu algoritması sunulmaktadır. Kapsamlı deneyler iki iyi bilinen veri seti üzerinde gerçekleştirilmiştir. Önerilen yaklaşım, tüm veri setleri boyunca seçilen toplam özellik sayısında %31-45 oranında azalma sağlayarak hesaplama maliyetlerini önemli ölçüde düşürmekte ve doğru teşhis oranını %95-97'ye kadar artırmaktadır.

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Cite This Article

Sermutlu, A., Dökeroğlu, T. (2025). A new multiobjective Harris Hawk Optimization algorithm for the diagnosis of breast cancer. Researcher, Cilt: 05 Sayı: 01, 17–25. https://ajindex.com/articles/1537220

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