A Pythagorean fuzzy set is characterized by values satisfying the condition that the square sum of the degree of membership and degree of non-membership is less than or equal to 1. As a generalized set, Pythagorean fuzzy sets have a close relationship with intuitionistic fuzzy sets. In this study, an algorithm is given that can select patients at risk of developing heart disease based on cardiovascular data. This given algorithm is created with Pythagorean fuzzy soft sets. The new algorithm is offered a medical decision-making method to assist in medical diagnosis. A medical case was examined as a real-life application to see if the proposed method is applicable. The real dataset which is called the Cleveland heart disease dataset has been chosen. In the application, the dataset is arranged as PFSS. In addition, the parameter set was determined and calculations were made in accordance with PFSS. A comparison table was created with the values obtained from these calculations. By choosing the maximum of the values obtained with the score function, the patient with the highest risk of developing heart disease was determined.