## 4 Discussion  
This current study presents a preoperative predictive model for early recovery from PPI within 3 months after RP. Age, MUL, and OIM were significantly different between the early-recovery group and the consistent group. In multivariate LR analyses, age and MUL were significant independent factors. Based on these results, a predictive model with LR analysis showed low to moderate diagnostic accuracy. Applying a machine learning algorithm, the predictive models showed improved diagnostic performance with higher AUCs compared with the model using LR, especially in the SVM algorithm. These models showed a good calibration between the estimated probability actual proportion of cases experiencing early recovery from PPI. Previous studies have suggested that older age, a higher BMI, the coexistence of medical comorbidities, a history of TURP, the presence of preoperative lower urinary tract symptoms, decreased MUL, surgery type, PV, and sacrificed neuromuscular bundles may be risk factors for urinary incontinence after RP [14; 16; 17; 31; 32; 33]. Among them, we focused on the preoperative factors and the measurements of pelvic skeletal muscles using MRI. Determining the preoperative factors related to the consistent status after RP and patients at a high risk of incontinence would be of value to clinicians when consulting patients prior to surgery and in explaining delays in postoperative urinary incontinence recovery. In the current study, the probability of commence recovery within 3 months after RP increased with MUL and decreased with age. A previous study found that maximal urethral closure decreased by approximately 27% after RP [34]. Patients with a longer MUL were more likely to maintain a longer intact urethra sphincter and, subsequently, a higher urethral closure pressure after RP [35]. In other words, a longer MUL was more likely to maintain sphincter function. Our results are consistent with previous findings of a significant relationship between MUL and continue recovery after RP. Age has been confirmed as a significant factor influencing a consistent status after RP [6; 7; 18; 30; 36; 37]. According to a study by [PERSON] et al., a patient's age at RP influenced the risk of incontinence in an exponential manner, particularly in long-term urinary incontinence [37]. Another study found that an age < 70 years was highly relevant to recovery from PPI within 3 months after RP [36]. This finding could be due to a general loss of ability to control urination with age. Concerning the complicated physiology of micturition involving various organs and the nervous system, it appears to be difficult to predict the status of commence after RP. Several intraoperative maneuvers have been also shown to affect commence states following post-prostatectomy, including neurovascular bundle-sparing surgery [34; 38; 39], bladder neck preservation [40], and novel reconstruction of tissue around the vesicourethral anastomosis [41]. Additionally, the surgeons' experience and skill are also involved in urinary outcomes after RP [42]. Accordingly, surgical technique could be individually tailored,  
Figure 2: ROC curve of each model. (**a**) Internal validation of KNN, (**b**) external validation of KNN, (**c**) internal validation of DT, (**d**) external validation of DT, (**e**) internal validation of LR, (**f**) external validation of LR, (**g**) internal validation of SVM, (**h**) external validation of SVM, (**i**) internal validation of RF, (**j**) external validation of RF. especially for high-risk patients with PPI. In addition to changing surgical techniques, preoperative machine learning-based predictive models are useful for preoperative and postoperative patient counseling and postoperative treatment planning.