In this paper, heart disease prediction modeled using partially observable markov decision process (POMDP) is proposed. In emergency, the patient is alerted through the doctor by fog computing. Ambulance sent to the location of patient at critical situations. The doctor gets the data through fog computing iFogSim. Fog computing in healthcare is a new area, which gains more attraction in research community. Many researches focus on cardiovascular disease i.e. heart disease. The important risk factor for cardiovascular disease is increase in blood viscosity. The highly viscous nature of blood does not allow the blood to flow creating a resistance in the blood flow. Heart disease risk factors are high blood pressure, obesity, diabetes, increased blood viscosity, etc. With the help of POMDP's states, observations, beliefs, probability transitions the patient health is noted. The POMDP model for heart disease prediction computes the policy approximation using states and timeslots. Rewards are tabulated using policy approximations over different iterations. © 2019 IEEE.