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Algorithms
Volume 19
Issue 4
10.3390/a19040301
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

A Physics-Guided Machine Learning Algorithm for Non-Ionizing Femur Fracture Classification from RF Spectral Data

by
Prince O. Siaw
1,
Yacine Chahba
1,
Ebenezer Adjei
1,
Ahmad Aldelemy
1,
Salamatu Ibrahim
1 and
Raed Abd-Alhameed
1,2,*
1
Faculty of Engineering and Digital Technologies, Bradford University, Bradford BD7 1DP, UK
2
Department of Information and Communication Engineering, Al-Farqadein University College, Basrah 61004, Iraq
*
Author to whom correspondence should be addressed.
Algorithms 2026, 19(4), 301; https://doi.org/10.3390/a19040301 (registering DOI)
Submission received: 14 February 2026 / Revised: 4 April 2026 / Accepted: 6 April 2026 / Published: 12 April 2026
(This article belongs to the Special Issue AI-Driven Engineering Optimization)
Versions Notes

Abstract

This paper presents a physics-guided machine learning algorithm for classifying femur fracture presence and subtype using non-ionising radiofrequency (RF) spectral data. Multi-sensor S-parameter responses were generated from a femur phantom model across 1.0–3.0 GHz, producing 104 specimens representing intact bone and three fracture geometries. An exploratory, effect-size-driven band-selection algorithm identified a compact discriminative region between 1.74 and 1.90 GHz. Interpretable classifiers, including k-nearest neighbours (KNN), decision trees, linear discriminant analysis, and Naïve Bayes, were evaluated under strict specimen-level hold-out protocols to prevent data leakage. The KNN algorithm achieved 99.3% frame-level accuracy and 100% specimen-level accuracy for binary fracture detection while maintaining strong robustness in multiclass subtype classification, validated through sensor ablation and leave-one-subtype-out testing. The results demonstrate that compact, interpretable algorithms operating on band-limited RF spectra can achieve reliable, radiation-free fracture classification, supporting future development of continuous and edge-deployable monitoring systems.
Keywords: RF sensing; S-parameters; fracture classification; machine learning algorithms; band selection; KNN; non-ionising diagnostics RF sensing; S-parameters; fracture classification; machine learning algorithms; band selection; KNN; non-ionising diagnostics

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MDPI and ACS Style

Siaw, P.O.; Chahba, Y.; Adjei, E.; Aldelemy, A.; Ibrahim, S.; Abd-Alhameed, R. A Physics-Guided Machine Learning Algorithm for Non-Ionizing Femur Fracture Classification from RF Spectral Data. Algorithms 2026, 19, 301. https://doi.org/10.3390/a19040301

AMA Style

Siaw PO, Chahba Y, Adjei E, Aldelemy A, Ibrahim S, Abd-Alhameed R. A Physics-Guided Machine Learning Algorithm for Non-Ionizing Femur Fracture Classification from RF Spectral Data. Algorithms. 2026; 19(4):301. https://doi.org/10.3390/a19040301

Chicago/Turabian Style

Siaw, Prince O., Yacine Chahba, Ebenezer Adjei, Ahmad Aldelemy, Salamatu Ibrahim, and Raed Abd-Alhameed. 2026. "A Physics-Guided Machine Learning Algorithm for Non-Ionizing Femur Fracture Classification from RF Spectral Data" Algorithms 19, no. 4: 301. https://doi.org/10.3390/a19040301

APA Style

Siaw, P. O., Chahba, Y., Adjei, E., Aldelemy, A., Ibrahim, S., & Abd-Alhameed, R. (2026). A Physics-Guided Machine Learning Algorithm for Non-Ionizing Femur Fracture Classification from RF Spectral Data. Algorithms, 19(4), 301. https://doi.org/10.3390/a19040301

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