Enhancing Breast Cancer Diagnosis: A Comparative Evaluation of Machine Learning Algorithms Using the Wisconsin Dataset

Yigitcan Cakmak; Ishak Pacal

doi:10.31181/jopi31202539

Authors

Yigitcan Cakmak Computer Engineering, Faculty of Engineering, Igdir University, 76000, Igdir, Turkey Author https://orcid.org/0009-0008-7227-9182
Ishak Pacal Computer Engineering, Faculty of Engineering, Igdir University, 76000, Igdir, Turkey Author https://orcid.org/0000-0001-6670-2169

DOI:

https://doi.org/10.31181/jopi31202539

Keywords:

Breast Cancer Detection, Machine Learning, Support Vector Machines, CatBoost, Gradian Boost, KNN, Wisconsin Dataset

Abstract

Breast cancer remains a leading cause of morbidity, particularly among women, underscoring the critical importance of early detection. In recent years, highly accurate machine learning algorithms have revolutionized breast cancer identification, significantly improving early diagnosis by analyzing tumor attributes to aid in detection and treatment decisions. This study evaluates seven machine learning algorithms using the Wisconsin breast cancer dataset, revealing that the Support Vector Machines (SVM) algorithm outperforms all others with an exceptional accuracy of 97.66%. These findings highlight the transformative potential of machine learning in clinical practice, offering healthcare professionals a powerful tool to enhance diagnostic precision, improve patient outcomes, and advance progress in oncology.

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