## 1 Introduction  
Clinical decision-making involves weighing clinical decisions according to the patient's condition and treatment method. CDSSs, which aim to provide personalized services to clinicians, staff, and patients in the medical field, have been the subject of continuous research for several years [1]. Some CDSS studies have provided promising results regarding technical sophistication, and some systems are being used in clinical practice [2, 3]. In other words, users can make decisions based on clinical data from electronic medical records (EMRs) similar to that of a target patient, ultimately reducing costs and improving medical quality [4]. Thus, CDSSs have been implemented to enable personalized precision medicine treatments [5, 6].  
Machine learning removes individual subjectivity and has the advantage of logically basing various methods and decisions on the data of a specific patient [7]. The reasons for a particular decision being made by such a system should be transparent to experts [8]. The combination of DT and healthcare enables monitoring, understanding, and optimization of all patient functions and can provide insight for continuous health and quality-of-life improvement [9]. Moreover, it can enable interoperability between physical and virtual environments through data analysis using artificial intelligence [10].  
Therefore, a DT approach, as a precision medicine technique, is considered to provide the best treatment by providing and utilizing decisive evidence from the results learned from the data [11]. Further, this approach is very advantageous in terms of effective disease prevention and low disease burden as the patient and the attending physician can select the optimal decision [12; 13].  
Prostate cancer is a common cancer among men worldwide [14]. Currently, research into the introduction of ML technology continues. Guidelines for the diagnosis, staging, and treatment are constantly changing, and the cycle of changing treatment guidelines is shortening [15; 16]. Consequently, clinicians find it difficult to apply new medical guidelines because they must learn new, constantly changing medical guidelines [17]. DT, therefore, is useful for providing personalized treatment and addressing these limitations. DT can use ML to predict the survival status of new patients using similar patterns from EMR data [18].  
Surgery for PCa is decided according to the biopsy and clinical stage, the progress of PCa, and whether BCR can be determined [19; 20; 21]. Therefore, a system that supports pathology and BCR prediction using biopsy data is required. In a CDSS using DT, biopsy information is input virtually, and the pathology and BCR can be predicted [22]. Therefore, because the progress of the cancer can be predicted, doctors can make optimal decisions, thereby minimizing costs and providing precision medical services [23].  
In this study, we propose a DT system to provide a treatment recommendation method for clinical decision-making in the PCa process. In the following section, the framework of the proposed DT system is presented, and the structure of the system is described. Furthermore, an ML method is described for providing a treatment method. A virtual model was constructed according to the PCa process, and the patient's treatment results were learned with ML using data from a clinical data warehouse (CDW). Finally, we developed the process and method of the DT-based model, which is an ML framework and is applied to the PCa process. Section 3 provides the results for the performance of the proposed ML system and DT-based predictive model. Section 4 presents the discussion, and Section 5 provides the conclusion.