Automated Diagnosis of Orthopedic Patients with Vertebral Column Disorders Using Advanced Mathematical Modeling

Authors

Chen Feng, Orthopedics department, HongQi Hospital Affiliated to Mu Danjiang Medical University, Mu Danjiang 157011, Hei Longjiang, China
Zhenhua Sun, Orthopedics department, HongQi Hospital Affiliated to Mu Danjiang Medical University, Mu Danjiang 157011, Hei Longjiang, China
Xinheng Dai, Orthopedics department, HongQi Hospital Affiliated to Mu Danjiang Medical University, Mu Danjiang 157011, Hei Longjiang, China
Hongli Wen, Foreign Language Teaching and Research Department, Mu Danjiang Medical University,Mu Danjiang 157011, Hei Longjiang, ChinaFollow

Abstract

Orthopedic disorders are multifactorial, making accurate diagnosis a significant challenge. This study introduces a novel method for classifying patients into three categories—normal, disc herniation, and spondylolisthesis—using biomechanical parameters derived from diagnostic datasets. To enhance classification accuracy, two meta-heuristic optimization algorithms—the Zebra Optimization Algorithm (ZOA) and Chaos Game Optimization (CGO)—are integrated with Adaptive Boosting (ADAC) and Light Gradient Boosting Machine (LGBM) classifiers. The experimental results reveal that ZOA significantly improves model performance, particularly in the ADAC classifier. The baseline ADAC model achieved a mean accuracy of 0.916, which increased to 0.952 after optimization with ZOA (referred to as the ADZO model). These findings highlight the potential of ZOA to enhance the predictive capabilities of classification models in orthopedic diagnosis. The proposed hybrid approach contributes to more accurate, efficient, and automated diagnosis systems, which can ultimately support clinicians in selecting appropriate treatment strategies.

Recommended Citation

Feng, Chen; Sun, Zhenhua; Dai, Xinheng; and Wen, Hongli (2025) "Automated Diagnosis of Orthopedic Patients with Vertebral Column Disorders Using Advanced Mathematical Modeling," Iraqi Journal for Computer Science and Mathematics: Vol. 6: Iss. 3, Article 34.
DOI: https://doi.org/10.52866/2788-7421.1304
Available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss3/34