Abstract
Background: In this study, based on machine-learning technology, we aim to develop a predictive model of the short-term prognosis of Korean patients who received spinal stenosis surgery. Methods: Using the data obtained from 112 patients with spinal stenosis admitted at N hospital from February to November, 2019, a predictive analysis was conducted for the pain index, reoperation, and surgery time. Results: Results show that the predicted area under the curve was 0.803, 0.887, and 0.896 for the pain index, reoperation, and surgery time, respectively, thereby indicating the accuracy of the model. Conclusion: This study verified that the individual characteristics of the patient and treatment characteristics during surgery enable a prediction of the patient prognosis and validate the accuracy of the approach. Further studies should be conducted to extend the scope of this research by incorporating a larger and more accurate dataset.
Highlights
Machine learning (ML) is a field of artificial intelligence that comprises various algorithms allowing the self-learning of data patterns using a computer [1]
ML-based prognosis predictive models were developed using the data obtained from 112 patients who received spinal stenosis surgery, and the prediction accuracy was determined
The evaluation of the prognosis predictive models showed that the surgery prognosis could be predicted
Summary
Machine learning (ML) is a field of artificial intelligence that comprises various algorithms allowing the self-learning of data patterns using a computer [1]. ML algorithms have been applied for the development of predictive models in the field of medicine owing to the emergence of big data, development of various algorithms, and gradual improvement in the computational power [3]. Several ML techniques, such as the gradient boosting machine, decision-making tree, random forest, and neural network, have already been proven effective for the treatment of neurosurgical patients from the viewpoint of a greater predictive power than conventional statistical modelling techniques [4]. The number of patients with spinal stenosis in Korea has increased owing to an increase in the average life expectancy. For treatment of spinal stenosis, non-surgical therapies are generally recommended and have been found to be effective; several studies have reported that the decompression surgery is more advantageous than non-surgical therapies [6,7]. In this study, based on machine-learning technology, we aim to develop a predictive model of the short-term prognosis of Korean patients who received spinal stenosis surgery
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