###### Abstract  
**Purpose**: Despite increasing rates of prostate cancer among men, prostate cancer risk assessments continue to rely on invasive laboratory tests like prostate-specific antigen and Gleason score tests. This study aimed to develop a noninvasive, data-driven risk model for patients to evaluate themselves before deciding whether to visit a hospital. **Materials and Methods**: To train the model, data from the National Health Insurance Sharing Service cohort datasets, comprising 347,575 individuals, including 1928 with malignant neoplasms of the prostate, 5 with malignant neoplasms of the penis, 18 with malignant neoplasms of the testis, and 14 with malignant neoplasms of the epididymis, were used. The risk model harnessed easily accessible inputs, such as history of treatment for diseases including stroke, heart disease, and cancer; height; weight; exercise days per week; and duration of smoking. An additional 286,727 public datasets were obtained from the National Health Insurance Sharing Service, which included 434 (0.15%) prostate cancer incidences. **Results**: The risk calculator was built based on Cox proportional hazards regression, and I validated the model by calibration using predictions and observations. The concordance index was 0.573. Additional calibration of the risk calculator was performed to ensure confidence in accuracy verification. Ultimately, the actual proof showed a sensitivity of 60 (60.5) for identifying a high-risk population. **Conclusions**: The feasibility of the model to evaluate prostate cancer risk without invasive tests was demonstrated using a public dataset. As a tool for individuals to use before hospital visits, this model could improve public health and reduce social expenses for medical treatment.  
c 2025 15 7845 16 May 2025 26 June 20  
health data plays an important role in informing individuals whether to alter their lifestyle habits or schedule hospital visits, depending on the risk factors.  
Before deciding to visit a hospital to obtain a PCa diagnosis, individuals would benefit from access to tools to determine their own health status. Several tools to assess the risk of PCa currently exist, such as the European Randomized Study of Screening for Prostate Cancer Risk Calculator, the Prostate Cancer Prevention Trial Risk Calculator, and the Korean Prostate Cancer Risk Calculator [6; 7; 8; 9]; however, these tools, including the prostate-specific antigen (PSA) level and Gleason scores, mainly utilize clinical data, such as laboratory tests and biopsy results, as risk calculator input [10; 11]. As these tools aim to ensure better treatment decisions, they are not suited for daily use by individuals seeking to better understand their risk profile. Previous studies have evaluated the use of a risk calculator for certain prostate cancer cases. [PERSON] et al. attempted to modify the risk calculator [8], while [PERSON] et al. studied extensive family histories to determine relative risks and reduce the risk of randomized prostate cancer screening tests [12].  
This highlights the need to develop tools that can predict the health status and risks of cancers such as PCa, using information that healthy individuals can collect in their daily lives. Such tools can be developed using information related to life histories, such as lifestyle and underlying diseases. For instance, stroke and hypertension could constitute information in the calculator used to check status. These health histories may explain the risk of developing the disease. Additionally, technological advancements have made it easier to collect personal lifestyle information that can be utilized to predict clinical status using various wearable devices [13].  
This study proposes a model that can calculate the risk of PCa based on the lifestyle of the public using national cohort data, as well as findings that may help individuals who wish to prioritize personal health management.