<![CDATA[Health insurance premium is one of the important elements in the insurance industry that needs to be calculated correctly so that insurance companies can minimize risks and losses. In this study, insurance premiums for stroke patients are calculated by utilizing the Markov Chain method. This method is used to model the movement of a patient's health condition over time, considering various conditions such as recovery, relapse, or death. Each condition is represented by a state in the Markov Chain model, and the transition between states is calculated based on patient history data and transition probabilities. Based on the modeling results, a more accurate premium estimation is obtained compared to conventional methods, as it is able to consider the dynamics of changing health conditions. This research provides important insights for the insurance industry in risk management as well as more optimal premium calculations for patients with chronic diseases such as stroke.]]>
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