Precision Diagnosis: Leveraging KNN for Breast Cancer Detection

Abstract

Breast cancer is a class of disease which is the most common type of cancer nowadays in women and this kind of cancer has millions of new diagnoses globally each year. This research study is focused on early diagnosis for raising cure rates and increasing survival rates among patients. This project will discuss the use of various machine learning models to predict breast cancer, which includes Logistic Regression, Naive Bayes, SVM, K-Nearest Neighbour (KNN), Decision Tree, and Random Forest. KNN outperformed other models with an accuracy of 98.54%, precision of 0.98, and an F1-score of 0.98. To translate this model into a real-world web application, a Flask based web interface was developed to be used by health professionals and patients with real-time predictions. Future work will involve optimization of models, refining features, and integrating the medical system to assist clinical decision-making