PREDICTION OF BREAST CANCER USING SIMPLE MACHINE LEARNING ALGORITHMS

Abstract

Breast cancer is one of the most common and life-threatening diseases affecting women worldwide. Early detection plays a crucial role in increasing survival rates and reducing treatment costs. However, traditional diagnostic procedures are time-consuming and largely dependent on the expertise of medical specialists. This study aims to assist healthcare professionals in diagnosing breast cancer by predicting tumor characteristics using basic machine learning techniques. Clinical data are utilized to classify tumors as either benign or malignant. Machine learning algorithms such as K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Logistic Regression are applied to the dataset. The models are developed, trained, and evaluated using Python and standard machine learning libraries. The proposed approach enables early detection and supports clinical decision-making by providing accurate, efficient, and reliable predictions.