Analysis and Visualization of Breast Cancer Prediction through Machine Learning Models

Abstract

This research presents an in-depth exploration of breast cancer prediction through the application of machine learning models, specifically focusing on Logistic Regression, K-Nearest Neighbors, Support Vector Classifier, 'Decision Tree Classifier, Random Forest Classifier, Gradient Boosting Classifier, AdaBoost Classifier, and XGBoost Classifier. The study utilizes a comprehensive dataset comprising clinical features extracted from Kaggle. Various algorithms are employed, and a meticulous analysis of precision, recall, F1-score, and accuracy is conducted to assess model performance. Through advanced visualization techniques and statistical analysis, the research provides insights into the effectiveness of machine learning models in predicting breast cancer. The outcomes of this study aim to contribute valuable knowledge to the field of medical diagnostics, emphasizing the importance of machine learning methodologies in enhancing breast cancer prediction and classification.