Prediction of breast cancer with 98% accuracy

TL;DR

This study improves breast cancer prediction accuracy by combining SMOTE with R Shiny, finding logistic regression to be the most effective algorithm on Kaggle data.

Contribution

It introduces a novel combination of SMOTE and R Shiny for enhanced breast cancer prediction accuracy, identifying the best algorithm among several tested.

Findings

Logistic regression outperformed other algorithms.

SMOTE improved prediction accuracy.

Evaluation using confusion matrices and ROC curves.

Abstract

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.