Anticipating Bosom Malignant Growth Utilizing Troupe AI Models

Abstract

Early detection of breast cancer significantly increases treatment success and survival rates.
Machine learning techniques offer promising tools for predicting breast cancer based on clinical data. This
research explores the effectiveness of multiple machine learning models Support Vector Machines (SVM),
Random Forest, Bagging, and AdaBoost classifiers for predicting breast cancer. Using a dataset containing
features extracted from breast cancer cell nuclei, we preprocess the data by encoding target variables,
handling missing values, and removing outliers. Model performance is evaluated based on accuracy,
precision, recall, and F1 score. Our findings show that ensemble learning techniques, particularly the Random
Forest and AdaBoost classifiers, outperformed other models, demonstrating high accuracy in breast cancer
prediction. These results suggest that ensemble methods provide robust predictive models for early diagnosis
in healthcare settings