Medical Data Analysis

This project focuses on classifying breast cancer tumors as benign or malignant using machine learning techniques. The well-known UCI Breast Cancer dataset was utilized for this study. Data preprocessing included handling missing values and applying feature scaling to ensure optimal model performance. Two models were developed and compared: Support Vector Machines (SVM) with different kernel functions, and a Multi-Layer Perceptron (MLP) neural network. The models were evaluated using key performance metrics including accuracy, precision, recall, F1-score, and the Area Under the ROC Curve (AUC). Additionally, the project incorporates comprehensive visualizations such as confusion matrices, performance comparison charts, and ROC curves to provide deeper insights into model behavior. The results demonstrate the effectiveness of machine learning algorithms in supporting medical diagnosis and improving classification accuracy.