  Abstract Cancer is a tumor that affects people worldwide, with a higher
incidence in females but not excluding males. It ranks among the top five
deadliest types of cancer, particularly prevalent in less developed countries
with deficient healthcare programs. Finding the best algorithm for effective
breast cancer prediction with minimal error is crucial. In this scientific
article, we employed the SMOTE method in conjunction with the R package Shiny
to enhance the algorithms and improve prediction accuracy. We classified the
tumor types as benign and malignant (B/M). Various algorithms were analyzed
using a Kaggle dataset, and our study identified the superior algorithm as
logistic regression. We evaluated algorithm performance using confusion
matrices to visualize results and the ROC Curve to obtain a comprehensive
measure of performance. Additionally, we calculated precision by dividing the
number of correct predictions by the total predictions Keywords Breast cancer,
Smote, Benign, Malignant.
