Breast Cancer Identification with Machine Learning Techniques

Abstract

Breast cancer is one of the most common and life-threatening diseases among women worldwide, and its early detection plays a vital role in improving survival rates. Traditional diagnostic methods, though effective, are often time-consuming and require expert interpretation. With the rapid advancement of artificial intelligence, machine learning (ML) techniques have emerged as powerful tools for medical diagnosis and prediction. This study explores the application of machine learning techniques for breast cancer recognition, focusing on data preprocessing, feature selection, and classification models. Various techniques such as Support Vector Machine (SVM), Random Forest, Logistic Regression, K-Nearest Neighbor (KNN) and K- Mean are evaluated to identify the most accurate approach for classification of benign and malignant tumors. The performance of the models is assessed using metrics such as accuracy, precision, recall, and F1-score. The findings demonstrate that machine learning techniques can provide high accuracy in breast cancer recognition, thereby supporting healthcare professionals in making reliable and timely decisions.