Development of a Machine-Learning Model of Short-Term Prognostic Prediction for Spinal Stenosis Surgery in Korean Patients

• Main Authors: Kyeong-Rae Kim, Hyeun Sung Kim, Jae-Eun Park, Seung-Yeon Kang, So-Young Lim, Il-Tae Jang
• Format: Article
• Language: English
• Published: MDPI AG 2020-10-01
• Series: Brain Sciences
• Subjects: machine learning; prediction; pilot study; spinal surgery; Korean
• Online Access: https://www.mdpi.com/2076-3425/10/11/764

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.