Skin Cancer Prediction Using KNN

Abstract

This Skin cancer is one of the most prevalent and life-threatening diseases worldwide, making early detection essential for effective treatment. This project aims to develop a skin cancer prediction system using the K-Nearest Neighbors (KNN) algorithm, a simple yet powerful machine learning technique. The dataset includes skin lesion images or structured data containing features like color, texture, and shape. Preprocessing techniques such as image resizing, normalization, and feature extraction are applied to enhance model accuracy. The KNN model is trained and tested to classify lesions as benign or malignant, with evaluation metrics including accuracy, precision, recall, and F1-score. This system provides a cost-effective, non-invasive, and accessible solution for preliminary skin cancer diagnosis, assisting dermatologists in early detection. Future enhancements may involve optimizing the K-value, improving dataset diversity for better generalization, and integrating real-time processing for immediate predictions. Additionally, developing a user-friendly web or mobile application can increase accessibility for both patients and healthcare professionals. The project emphasizes the potential of machine learning in medical diagnostics, reducing dependency on expensive and time-consuming traditional testing methods. By improving early detection rates, this system can significantly contribute to better patient outcomes and lower mortality rates associated with skin cancer.