Abstract
Introduction
The aim of this study, was to develop an artificial neural networks (ANNs) model for predicting successful surgery outcome in lumbar disc herniation (LDH).
Materials and Methods
An ANN model and a logistic regression (LR) model were used to predict outcomes. The age, gender, duration of symptoms, smoking status, surgical level, visual analog scale of leg/back pain, the Zung depression scale, and the Japanese Orthopaedic Association score, were determined as the input variables for the established ANN model. The Macnab classification was used for outcome assessment. ANNs on data from LDH patients, who had surgery, were trained to predict 2-year successful discectomy using several input variables. Sensitivity analysis to the established ANN model was used to identify the relevant variables. For evaluating the two models, the area under a receiver operating characteristic curve, accuracy rate of predicting, and Hosmer-Lemeshow (H-L) statistics were considered.
Results
A total of 203 (96 male, 107 female, mean age 48.3 ± 9.8 years) patients were categorized into training, testing, and validation datasets consisting of 101, 51, and 51 cases, respectively. Surgical successful outcome was categorized as: excellent, 32.0%; good, 40.9%; fair, 20.7%; and poor, 6.4% at 2-year follow-up. Compared with the LR model, the ANN model showed better results: accuracy rate, 95.8%; H-L statistic, 41.5%; and AUC, 0.82% of patients, respectively.
Conclusion
The findings show that ANNs can predict successful surgery outcome with a high level of accuracy in LDH patients. Such information is of use in the clinical decision-making process.
None declared
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