## 6 Conclusions  
Currently, data mining techniques are used extensively in health care to analyze and identify useful information to construct assistive diagnostic models that can help medical professionals perform diagnosis and treatment. Patients with mild LSS experience pain in the lower back or in the buttocks. Patients with severe LSS may experience weakness in the lower extremities while walking or dysfunction during excretion. Some patients with LSS may recover effectively after receiving conservative treatment for a period of time. However, other patients with LSS must receive surgical treatment after conservative treatment fails. Delaying required treatment affects the timeliness and efficacy of the treatment. [PERSON] et al. (2000) noted that predictors of treatment outcomes for surgery or conservative treatment are unavailable. Regarding the performance of the 12 models constructed in this study, all models have an AUC performance capability beyond 0.8. Most of the models have a model performance of 0.9 or better. Among the constructed models, the model combining LGR, C5.0 DT and a BPNN exhibited the best model performance, with a testing accuracy rate of 94.87% and an AUC of 0.952. Regarding the hidden layer configuration of a BPNN, models adopting two levels of hidden layers exhibited better model performance. For feature selection, the prediction model combining LGR and C5.0 DT for feature selection exhibited the best performance. Several critical features were obtained using this model: reduction of the intervertebral disc height, age, systolic pressure, leg pain, gender, etc. However, this study was limited by practical constraints such as the small number of data available and categorical class imbalances; in the works are the plans for the next phase of this study for further verifications of the predictive models by acquiring a larger dataset such as Taiwan's National Health Insurance Database. Although certain limitations remained for these factors for predicting whether patients with LSS ultimately underwent surgical treatment, they can still serve as references for medical care personnel. These factors can also serve as an adjunct diagnostic tool for diagnosing LSS. In the current study, sociodemographic, behavioral, and illness-related factors were collected for academic research, and these selected factors can be used to perform careful and comprehensive evaluation of suitable treatment plans to provide precision medical services. In addition, the trust level between the hospital and patients and their family members could be enhanced to lower unnecessary medical risks. Furthermore, close integration of data mining and health care information can enhance medical service quality and lower additional costs.  
Conceptualization, L.-P.T.; methodology, L.-P.T., Y.-C.P., Y.-S.C. and T.-H.H.; software design, Y.-S.C., T.-H.H.; collected the data, L.-P.T. [PERSON] and [PERSON]; writing--original draft preparation, Y.-S.C. and Y.-K.O.; writing--review and editing, Y.-K.O., T.-H.H. and L.-P.T. All authors have read and agreed to the published version of the manuscript.  
This study was funded by the St. Martin De Porres Hospital (P1903).  
We would like to thank anonymous reviewers and the editors for their comments.  
The authors confirm that there are no conflicts of interest in this current study.