Abstract
Topological Data Analysis (TDA) is based on the principle that data has shape and meaning. This method has been shown to increase the efficiency of processes across several fields such as health care and computational biology. In this research, to emphasize the importance of TDA in medical field, the mapper algorithm was used for predicting heart disease by using two UCI heart disease datasets (Cleveland and Statlog). To guide the exploration, we selected nine significant features in each dataset; also, we used tri-dimensional SVD filter to improve the filtering process. As a result, we have observed an accuracy of 99.32% in the Cleveland dataset and 99.62% in the Statlog dataset in predicting heart disease.