Evaluating Machine Learning Classifiers for Breast Cancer Diagnosis

The effectiveness of many machine learning classifiers, such as Decision Trees, Support Vector Machines (SVM), and k - Nearest Neighbors (k-NN), in identifying breast cancer is assessed in this project. To identify the best model, each classifier is evaluated for accuracy, sensitivity, and specificity using a labeled dataset of breast tissue samples. The results demonstrate that while Decision Trees give medical professionals interpretability, Support Vector Machines (SVM) offer great accuracy in classifying cancers. This work emphasizes how crucial classifier selection is for diagnostic applications and describes how machine learning could assist with detecting cancer earlier, providing patients with precise and rapid diagnostic tools.