Tehran University of Medical Sciences Journal of Biostatistics and Epidemiology 2383-4196 10 1 2024 12 01 82 97 EN abbas Rezaianzadeh 1Colorectal Research Center, Shiraz University of Medical Sciences. Shiraz, Iran. Samane Nematolahi 2Non Communicable Diseases Research Center, Bam University of Medical Sciences, Bam, Kerman, Iran. maryam jalali 1Colorectal Research Center, Shiraz University of Medical Sciences. Shiraz, Iran. Shayan Rezaeianzadeh 3Student Research Committee, Shiraz University of Medical Sciences. Shiraz, Iran. Masoumeh Ghoddusi Johari 4Breast Disease Research Center, Shiraz University of Medical Sciences. Shiraz, Iran. Seyed Vahid Hosseini 1Colorectal Research Center, Shiraz University of Medical Sciences. Shiraz, Iran. 2024 05 19 2024 08 26 Background: Hypertension is a serious chronic disease and an important risk factor for many health problems. this study aimed to investigate the factors associated with hypertension using a decision-tree algorithm. Methods: this cross-sectional study was conducted in Kharameh city between 2014-2017 through census. The study included 2510 hypertensive and 7840 non-hypertensive individuals. 70% of the cases were randomly allocated to the training dataset for establishing the decision tree, while the remaining 30% were used as the testing dataset for performance evaluation of the decision-tree. Two models were assessed. In the first model (model I), 15 variables including age, gender, body mass index, years of education, Occupation status, marital status, family history of hypertension, physical activity, total energy, number of meals, salt, oil type, drug use, alcohol use and smoke entered in to the model. in the second model (model II) 16 variables including age, gender, BMI and Blood factors as HCT, MCHC, PLT, FBS, BUN, CERAT, TG, CHOL, ALP, HDL, GGT, LDL and SG were considered. a receiver operating characteristic (ROC) curve was applied to assess the validation of the models. Results: The accuracy, sensitivity, specificity, and area under the ROC curve (AUC) are important metrics to evaluate the performance of a decision tree model. For model I, the accuracy, sensitivity, specificity and area under the ROC curve (AUC) value were 79.24%, 82.41%, 78.24% and 0.80, respectively. for model II, the corresponding values were 79.50%, 81.03%, 79.02% and 0.80, respectively. Conclusion: We have suggested a decision tree model to identify the risk factors associated with hypertension. This model can be useful for early screening and improving preventive and curative health services in health promotion. https://jbe.tums.ac.ir/index.php/jbe/article/view/1432