ROC Curve Plotting Program with MATLAB Implementation

This MATLAB-based ROC curve plotting program provides a sophisticated solution for visualizing classifier performance metrics. The implementation utilizes MATLAB's statistical toolbox functions while incorporating custom algorithms for duplicate detection and data validation. Key technical features include: - Automated ROC curve generation using MATLAB's perfcurve function with customizable parameters - Advanced search algorithm that employs binary search techniques for efficient data retrieval - Duplicate elimination system utilizing hash-based data structures for O(1) lookup operations - Interactive plotting interface with options for AUC calculation and confidence interval display The program architecture implements threshold optimization algorithms that systematically evaluate sensitivity and specificity across all classification thresholds. Data preprocessing modules include normalization routines and outlier detection mechanisms to ensure analytical accuracy. For technical implementation, the core functionality leverages vectorized operations for optimal computational performance, while the GUI components utilize MATLAB's App Designer framework for cross-platform compatibility. The code includes comprehensive error handling and supports multiple input formats including CSV, Excel, and native MATLAB data structures.