• Ms. Tejaswi Yamala
• Mr.CH.Dinesh

Breast Cancer, identified by the Breast Cancer Institute (BCI) as one of the most perilous diseases affecting women globally, underscores the critical need for effective detection. Early diagnosis, particularly in the initial stages, significantly contributes to saving lives. Platforms such as cancer.net provide tailored guides covering a spectrum of over 120 cancer types and related hereditary syndromes.In the realm of breast cancer detection, machine learning techniques take center stage. This work introduces an innovative approach – the adaptive ensemble voting method – for diagnosing breast cancer, leveraging the Wisconsin Breast Cancer database. The primary objective is to scrutinize and elucidate how logistic algorithms can offer superior solutions when collaborating with ensemble machine learning algorithms for breast cancer diagnosis, even in scenarios where variables are reduced.

Breast Cancer, Adaptive Ensemble,Cancer Database, Syndromes

"Breast Cancer Diagnosis Using Adaptive Voting Ensemble Machine Learning Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 2, page no.e286-e289, February-2024, Available :http://www.jetir.org/papers/JETIR2402439.pdf

"Breast Cancer Diagnosis Using Adaptive Voting Ensemble Machine Learning Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 2, page no. ppe286-e289, February-2024, Available at : http://www.jetir.org/papers/JETIR2402439.pdf