ROC Curve Analysis and AUC Value Calculation with MATLAB Implementation

This MATLAB code provides a comprehensive solution for Receiver Operating Characteristic (ROC) curve analysis and Area Under Curve (AUC) computation. The implementation features automated threshold scanning across classification scores, calculating true positive rates (sensitivity) and false positive rates (1-specificity) at each interval. The AUC is computed using the trapezoidal integration method, ensuring accurate performance metric quantification for binary classification systems. The algorithm efficiently handles both categorical and continuous variable types, incorporating data normalization procedures and missing value handling mechanisms. Key functions include score probability calibration, confusion matrix generation at optimal thresholds, and graphical ROC visualization with performance statistics overlay. Well-documented with inline comments explaining each computational step, the code structure facilitates easy modification for specialized datasets. The implementation includes error checking for input validation and supports both single-feature analysis and multi-dimensional data processing through vectorized operations. This makes it particularly valuable for machine learning model evaluation, medical diagnostic testing, and statistical pattern recognition applications where classification performance validation is critical. The modular design allows seamless integration with existing MATLAB workflows, featuring customizable output formats including precision-recall curves and F-score calculations. Computational efficiency is maintained through preallocation techniques and optimized sorting algorithms for large-scale dataset processing.