Breast Cancer Detection Using KNN Algorithm

Abstract

Breast cancer is a leading cause of mortality in women, necessitating early and accurate detection. This study investigates the K-Nearest Neighbors (KNN) algorithm for breast cancer classification using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset [9]. Preprocessing steps included normalization and feature selection, followed by KNN implementation in Python with hyperparameter tuning. Evaluation metrics (accuracy, precision, recall, F1-score) demonstrated KNN’s effectiveness, though limitations like sensitivity to imbalanced data were noted. The findings suggest KNN as a viable tool for breast cancer detection, with future work exploring ensemble methods for enhanced performance.