Objectives: This study compares a data mining technique, the Artificial Neural Networks (ANNs) method, with Logistic Regression in order to analyze patient churn patterns and the characteristics of patient construction in primary health care patients. It is aimed at establishing a forecast model for the prediction of possible patient churn patterns in primary health care clinics, and could provide pertinent information to enable the development of marketing strategies, serving as an important reference for decision makers. Methods: Through analysis of the patient database of the out-patient service in a primary health care clinic using an ANNs method, the Back-Propagation Network (BPN) method, data were used in preprocessing and data mining, and the network model establishes. Results: The findings of this study include: (1) Logistic Regression analysis-of the variables used, only age shows a significant difference, and the correct classification rate was 66.2%. (2) BPN analysis-the correct classification rate was up to 71.3%. Correlation analysis shows that only the patient category parameter shows negative correlation; all other parameters show positive correlation. Patient’s gender was shown to be the most important variable in sensitivity analysis, and the male gender has a higher churn rate. After the influence of each variable has been indentified, primary health care managers are then able to understand which factors should be made priority. Conclusion: Comparison of the two forecast models shows that BPN has a higher correct classification rate on the whole than has Logistic Regression. This study demonstrates that the BPN model can be used to identify the potential churn of patients and is important in practical applications.