Choice between Surgery and Conservative Treatment for Patients with Lumbar Spinal Stenosis: Predicting Results through Data Mining Technology

Abstract

Currently, patients with lumbar spinal stenosis (LSS) have two treatment options: nonoperative conservative treatment and surgical treatment. Because surgery is invasive, patients often prefer conservative treatment as their first choice to avoid risks from surgery. However, the effectiveness of nonoperative conservative treatment for patients with LSS may be lower than expected because of individual differences. Rules to determine whether patients with LSS should undergo surgical treatment merits exploration. In addition, without a decision-making system to assist patients undergoing conservative treatment to decide whether to undergo surgical treatment, medical professionals may encounter difficulty in providing the best treatment advice. This study collected medical record data and magnetic resonance imaging diagnostic data from patients with LSS, analyzed and consolidated the data through data mining techniques, identified crucial factors and rules affecting the final outcome the patients with LSS who opted for conservative treatment and ultimately underwent surgical treatment, and, finally, established an effective prediction model. This study applied logistic regression (LGR) and decision tree algorithms to extract the crucial features and combined them with back propagation neural networks (BPNN) and support vector machines (SVM) to establish the prediction model. The crucial features obtained are as follows: reduction of the intervertebral disc height, age, blood pressure difference, leg pain, gender, etc. Among the models predicting whether patients with LSS ultimately underwent surgical treatment, the model combining LGR and the decision tree for feature selection with a BPNN has a testing accuracy rate of 94.87%, sensitivity of 0.9, specificity of 1, and area under the receiver operating characteristic curve of 0.952. Adopting these data mining techniques to predict whether patients with LSS who opted for conservative treatment ultimately underwent surgical treatment may assist medical professionals in reaching a treatment decision and provide clearer treatment. This may effectively mitigate disease progression, aid the goals of precision medicine, and ultimately enhance the quality of health care.

Keywords

• lumbar spinal stenosis (LSS)
• data mining
• back propagation neural network
• decision tree
• support vector machine
• prediction