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Dergiler / Bitlis Eren Üniversitesi Fen Bilimleri Dergisi / 2022 / Cilt: 11 - Sayı: 2

Breast Cancer Diagnosis with Machine Learning Techniques

Ahmet Burak TATAR
Halime DOĞAN
Alper Kadir TANYILDIZI
Beyda TAŞAR
Sayfa
594–603
Yayın Tarihi
2022-10-12
DOI
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

According to the results obtained, the highest success rate with 99.42% was obtained by using neighborhood component analysis and nearest neighbor classification algorithm method.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Cancer deaths are one of the highest rates of death.
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Temel Bulgular & Veri:Although breast cancer is commonly associated with women, it is sometimes seen in men, and the mortality rate for men with breast cancer may be higher.
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Sonuç & Katkı:According to the results obtained, the highest success rate with 99.42% was obtained by using neighborhood component analysis and nearest neighbor classification algorithm method.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Alan Parametreleri

Abstract

Cancer deaths are one of the highest rates of death. Although breast cancer is commonly associated with women, it is sometimes seen in men, and the mortality rate for men with breast cancer may be higher. The importance of early detection and treatment of breast cancer cannot be overstated. Cancer is diagnosed at an early stage thanks to expert systems, artificial intelligence, and machine learning approaches, and data analysis makes life easier for healthcare professionals. The nearest neighbor method, principal component analysis, neighborhood component method approaches were employed to detect breast cancer in this study."Breast Cancer Wisconsin Diagnostic" database was used to create and test the approach. According to the results obtained, the highest success rate with 99.42% was obtained by using neighborhood component analysis and nearest neighbor classification algorithm method.

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Bu Makaleye Atıf Yap

TATAR, A. B., DOĞAN, H., TANYILDIZI, A. K., TAŞAR, B. (2022). Breast Cancer Diagnosis with Machine Learning Techniques. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, Cilt: 11 - Sayı: 2, 594–603. https://ajindex.com/articles/466518

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Kaynakça (13)

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