  Topological data analysis is a relatively new branch of machine learning that
excels in studying high dimensional data, and is theoretically known to be
robust against noise. Meanwhile, data objects with mixed numeric and
categorical attributes are ubiquitous in real-world applications. However,
topological methods are usually applied to point cloud data, and to the best of
our knowledge there is no available framework for the classification of mixed
data using topological methods. In this paper, we propose a novel topological
machine learning method for mixed data classification. In the proposed method,
we use theory from topological data analysis such as persistent homology,
persistence diagrams and Wasserstein distance to study mixed data. The
performance of the proposed method is demonstrated by experiments on a
real-world heart disease dataset. Experimental results show that our
topological method outperforms several state-of-the-art algorithms in the
prediction of heart disease.
