Replication Data and Code for "Phase-Domain Radiofrequency Signatures for Non-Invasive Bone Fracture Detection: A Two-Dimensional Computational Proof-of-Concept Study Using FDTD Simulation and Machine Learning

This repository contains all materials required to reproduce the results in the paper "Phase-Domain Radiofrequency Signatures for Non-Invasive Bone Fracture Detection: A Two-Dimensional Computational Proof-of-Concept Study Using FDTD Simulation and Machine Learning" (v20). It includes: Pre-computed 486-dimensional feature matrices for the original 27,088 samples Monte Carlo healthy class expansion (50 anatomically varied configurations, +1,600 samples) Group-wise 5-fold cross-validation fold assignments (no leakage) Monte Carlo generation and re-evaluation scripts Key results files (Before/After Monte Carlo comparison) All figures and supplementary tables The dataset enables independent reproduction of all classification results (AUC = 0.943 after MC expansion, specificity = 86.2%).