Machine-Learning-Based Biomechanical Feature Analysis for Orthopedic Patient Classification with Disc Hernia and Spondylolisthesis
Abstract
1. Introduction
2. Materials and Methods
3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
ML | Machine Learning |
IVD | Lumbar Intervertebral Disc |
CT | Computed Tomography |
THP | Total Hip Arthroplasty |
LBP | Low Back Pain |
CNN | Convolutional Neural Network |
MRI | Magnetic Resonance Imaging |
AUC | Area Under the Curve |
ROC | Receiver Operating Characteristic |
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Model | Accuracy | AUC | Recall | Precision | F1 | Kappa | MCC | TT (Sec) |
---|---|---|---|---|---|---|---|---|
Logistic Regression | 0.8576 | 0.9294 | 0.8576 | 0.8683 | 0.8590 | 0.6840 | 0.6910 | 0.2380 |
Ridge Classifier | 0.8437 | 0.8922 | 0.8437 | 0.8524 | 0.8359 | 0.6188 | 0.6385 | 0.0050 |
Linear Discriminant Analysis | 0.8437 | 0.8922 | 0.8437 | 0.8534 | 0.8377 | 0.6255 | 0.6430 | 0.0050 |
Gradient Boosting Classifier | 0.8338 | 0.9106 | 0.8338 | 0.8373 | 0.8329 | 0.6180 | 0.6230 | 0.0190 |
Extra Trees Classifier | 0.8299 | 0.9167 | 0.8299 | 0.8302 | 0.8237 | 0.5914 | 0.6009 | 0.0240 |
Random Forest Classifier | 0.8251 | 0.9078 | 0.8251 | 0.8261 | 0.8232 | 0.5934 | 0.5976 | 0.0270 |
Light Gradient Boosting Machine | 0.8208 | 0.8935 | 0.8208 | 0.8233 | 0.8181 | 0.5838 | 0.5894 | 0.0680 |
K Neighbors Classifier | 0.8121 | 0.8901 | 0.8121 | 0.8221 | 0.8128 | 0.5771 | 0.5842 | 0.0120 |
Quadratic Discriminant Analysis | 0.7931 | 0.9185 | 0.7931 | 0.8615 | 0.7985 | 0.5947 | 0.6362 | 0.0050 |
Naive Bayes | 0.7792 | 0.8823 | 0.7792 | 0.8286 | 0.7847 | 0.5494 | 0.5772 | 0.0050 |
Decision Tree Classifier | 0.7792 | 0.7474 | 0.7792 | 0.7828 | 0.7785 | 0.4962 | 0.4994 | 0.0060 |
Ada Boost Classifier | 0.7606 | 0.8293 | 0.7606 | 0.7634 | 0.7563 | 0.4421 | 0.4501 | 0.0140 |
SVM-Linear Kernel | 0.7002 | 0.8865 | 0.7002 | 0.7062 | 0.6489 | 0.2789 | 0.3289 | 0.0060 |
Dummy Classifier | 0.6773 | 0.5000 | 0.6773 | 0.4587 | 0.5470 | 0.0000 | 0.0000 | 0.0040 |
Model | Accuracy | AUC | Recall | Precision | F1 | Kappa | MCC | TT (Sec) |
---|---|---|---|---|---|---|---|---|
Random Forest Classifier | 0.9083 | 0.9783 | 0.9083 | 0.9186 | 0.9066 | 0.8623 | 0.8689 | 0.0310 |
Gradient Boosting Classifier | 0.8989 | 0.0000 | 0.8989 | 0.9067 | 0.8979 | 0.8482 | 0.8528 | 0.0520 |
Extra Trees Classifier | 0.8956 | 0.9833 | 0.8956 | 0.9030 | 0.8942 | 0.8432 | 0.8478 | 0.0310 |
Light Gradient Boosting Machine | 0.8891 | 0.9681 | 0.8891 | 0.8997 | 0.8875 | 0.8335 | 0.8401 | 0.2950 |
Decision Tree Classifier | 0.8634 | 0.8978 | 0.8634 | 0.8758 | 0.8608 | 0.7949 | 0.8034 | 0.0080 |
Logistic Regression | 0.8446 | 0.0000 | 0.8446 | 0.8568 | 0.8451 | 0.7667 | 0.7720 | 0.0330 |
Ridge Classifier | 0.8225 | 0.0000 | 0.8225 | 0.8354 | 0.8148 | 0.7336 | 0.7439 | 0.0080 |
Linear Discriminant Analysis | 0.8225 | 0.0000 | 0.8225 | 0.8286 | 0.8201 | 0.7335 | 0.7385 | 0.0090 |
Naive Bayes | 0.7875 | 0.9115 | 0.7875 | 0.7946 | 0.7806 | 0.6813 | 0.6895 | 0.0100 |
Quadratic Discriminant Analysis | 0.7872 | 0.0000 | 0.7872 | 0.7967 | 0.7846 | 0.6804 | 0.6871 | 0.0090 |
K Neighbors Classifier | 0.5943 | 0.7767 | 0.5943 | 0.6310 | 0.5959 | 0.3910 | 0.4013 | 0.0120 |
Ada Boost Classifier | 0.5106 | 0.0000 | 0.5106 | 0.5668 | 0.4622 | 0.2668 | 0.3048 | 0.0190 |
SVM-Linear Kernel | 0.4200 | 0.0000 | 0.4200 | 0.3752 | 0.2916 | 0.1230 | 0.1951 | 0.0100 |
Dummy Classifier | 0.3175 | 0.5000 | 0.3175 | 0.1009 | 0.1531 | 0.0000 | 0.0000 | 0.0070 |
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Nasef, D.; Nasef, D.; Sawiris, V.; Girgis, P.; Toma, M. Machine-Learning-Based Biomechanical Feature Analysis for Orthopedic Patient Classification with Disc Hernia and Spondylolisthesis. BioMedInformatics 2025, 5, 3. https://doi.org/10.3390/biomedinformatics5010003
Nasef D, Nasef D, Sawiris V, Girgis P, Toma M. Machine-Learning-Based Biomechanical Feature Analysis for Orthopedic Patient Classification with Disc Hernia and Spondylolisthesis. BioMedInformatics. 2025; 5(1):3. https://doi.org/10.3390/biomedinformatics5010003
Chicago/Turabian StyleNasef, Daniel, Demarcus Nasef, Viola Sawiris, Peter Girgis, and Milan Toma. 2025. "Machine-Learning-Based Biomechanical Feature Analysis for Orthopedic Patient Classification with Disc Hernia and Spondylolisthesis" BioMedInformatics 5, no. 1: 3. https://doi.org/10.3390/biomedinformatics5010003
APA StyleNasef, D., Nasef, D., Sawiris, V., Girgis, P., & Toma, M. (2025). Machine-Learning-Based Biomechanical Feature Analysis for Orthopedic Patient Classification with Disc Hernia and Spondylolisthesis. BioMedInformatics, 5(1), 3. https://doi.org/10.3390/biomedinformatics5010003