  A model's interpretability is essential to many practical applications such
as clinical decision support systems. In this paper, a novel interpretable
machine learning method is presented, which can model the relationship between
input variables and responses in humanly understandable rules. The method is
built by applying tropical geometry to fuzzy inference systems, wherein
variable encoding functions and salient rules can be discovered by supervised
learning. Experiments using synthetic datasets were conducted to investigate
the performance and capacity of the proposed algorithm in classification and
rule discovery. Furthermore, the proposed method was applied to a clinical
application that identified heart failure patients that would benefit from
advanced therapies such as heart transplant or durable mechanical circulatory
support. Experimental results show that the proposed network achieved great
performance on the classification tasks. In addition to learning humanly
understandable rules from the dataset, existing fuzzy domain knowledge can be
easily transferred into the network and used to facilitate model training. From
our results, the proposed model and the ability of learning existing domain
knowledge can significantly improve the model generalizability. The
characteristics of the proposed network make it promising in applications
requiring model reliability and justification.
