## 5 Discussion  
Class imbalance is where the classes are not represented equally among the data. It occurs in many settings such as medicine and product defect rates in manufacturing industries and has been considered an important issue recently [27]. In machine learning, if training were performed with imbalanced data, the resulting models would be biased towards predicting the majority class rather than the minority [28]. This study is a binary classification problem with imbalanced data, and classifier performance from random over-sampling is better than from random under-sampling [29], therefore random over-sampling was used in this study, namely, SMOTE from R software, to prepare the data for building the models. While the data shows 81 individuals who underwent surgery and 22 who did not, the objective of this study was to build a prediction model with appropriate factors to assist doctors in making treatment recommendations to patients. Regarding network architecture, the BPNN is a type of multilayer feed forward network that can be used to solve nonlinear problems. It is the most widely used supervised learning network [24]. Therefore, the researchers of this study referred to neural network theory; selected features by using data sets from a C5.0 DT and combination of LGR and C5.0 DT; imported the selected key factors (i.e., imaging diagnostic data and medical record data) into a BPNN, C5.0 DT, and SVM for subsequent prediction; adjusted the parameter values of each classifier for optimization; and ultimately constructed the prediction models. Figure 3: Rules extracted from the C5.0 DT (70% data for training and 30% for testing). The dependent variable of the LGR model must be a binary variable. Hence, LGR is suitable for use in medical health care data, which are mainly binary. This study revealed that combining LGR and C5.0 DT feature selection methods enhanced the C5.0 DT and identified the crucial features to be disc height reduction, age, diastolic blood pressure, leg pain, and gender. Reducing the height of the intervertebral disk may cause lumbar foraminal stenosis, which is highly related to LSS symptoms such as neurogenic claudication, weakness, and a feeling of heavy legs [2; 6; 8]. Leg pain may be caused by lumbago, hypesthesia, paresthesia of the legs, and ataxia from lumbar foraminal stenosis [2; 6]. LSS symptoms are highly related to neural compression and local vascular deficiency [30]. Furthermore, [PERSON] et al. indicated that the VAS, Low Back Outcome Score, and reductions in claudation and leg pain are essential factors for evaluating surgical outcomes [21]. Underlying subclinical vascular factors may be involved in complaints from patients with LSS. Degenerative changes in the spine are the most common cause of spinal stenosis [9], and advanced age is highly correlated with the onset of degenerative diseases of the lumbar spine. According to [PERSON] et al. [31], 50% of the population older than 65 years is expected to experience LBP from a reduced height of the intervertebral disc, as detected through MRI. [PERSON] assessed the treatment outcomes of undergoing no operations and receiving surgical treatment among 139 patients with LSS and reported that older patients appeared to have worse outcomes 5 years earlier for LSS procedures [4]. From a medical perspective, age is in fact a main factor in lumbar spine degeneration and has been demonstrated in the literature to have a direct effect on LSS symptoms. While the age of 77 years as a rule generated by this study's best-performing logarithm warrants further investigation, this rule is derived from real-world data in conjunction with consideration for other factors such as intervertebral disc height, blood pressure, joint and muscle pain, etc. This is akin to clinical practice, in which doctors would make treatment recommendations based on evaluating patients' subjective symptoms and objective examinations.