An Ensemble Machine Learning Model for Breast Cancer

An Ensemble Machine Learning Model for Breast Cancer

C.A. Dishanth, Kuppala Sai Rishi, Shaik Abdul Niyaz, C.H. Jagadeesh, P. Harsha Vardhan, Dr. Ragaventhiran

Abstract:

Almost 1 crore people died from cancer last year, with breast cancer accounting for 22.6% of all cancer-related deaths worldwide (BC). In India, 14.7% of cancer cases are of the BC variety, which affects women more frequently than other cancers. Many studies have been done on the subject of early BC detection, which can aid with timely treatment initiation and a reduction in mortality. Roughly 86% of cases that are diagnosed are appropriately diagnosed. Machine learning techniques are useful for classifying data. Particularly in the realm of medicine, where those techniques are frequently utilized in diagnosis and analysis for decision-making.

This study conducted data visualization and performance evaluations of the Support Vector Classifier (SVC), Decision Tree Classifier, Gaussian Naive Bayes (GNB), K Nearest Classifier (k-NN), Logistic Regression and Linear discriminant analysis. The major goal is to assess each algorithm’s efficiency and effectiveness in terms of accuracy and precision in the classification of data. Our goal is to evaluate several Machine Learning techniques for early, effective, and accurate detection.