The annual healthcare expenditure of United States is increasing at a tremendous rate. The decisions made by people concerning their health enforces the government’s involvement in encouraging better health behavior through predefined strategies. The poor health behavior often leads to an unhealthy community which further leads to a poor health nation. The CDC administers Behavioral Risk Factor Surveillance System (BRFSS) survey annually. This paper aims to curb the poor decision making in health behavior utilizing 2016 SMART BRFSS data. Using data mining approach, predictive model is developed by analyzing the pattern of decision making of an individual and how it affects the health behavior. This model can aid future BRFSS surveys in better understanding the responses and provide an insight into the factors affecting these decisions. Further, the model will help in targeting poor decision makers to focus on their health behavior and prevent these decisions leading to major illnesses.