The Bayesian model is becoming increasingly important in medical statistics due to its ability to incorporate prior information and handle incomplete data. This study develops a Bayesian regression model in R to predict the risk of heart disease recurrence after hospital discharge. The model is evaluated using the ROC curve and AUC index. The results indicate that Bayesian inference allows for flexible updates based on new data and supports risk prediction with relatively high accuracy. This model can help medical and pharmacy students build similar models for other diseases.