• Statistical hypothesis testing
• Machine Learning
• Learning Classifier Systems
• Decision Trees

This thesis presents a new ML algorithm, HCS, taking
inspiration from Learning Classifier Systems, Decision Trees and
Statistical Hypothesis Testing, aimed at providing clearly
understandable models of medical datasets. Analysis of medical
datasets has some specific requirements not always fulfilled by
standard Machine Learning methods. In particular, heterogeneous
and missing data must be tolerated, the results should be easily
interpretable. Moreover, often the combination of two or more
attributes leads to non-linear effects not detectable for each
attribute on its own. Although it has been designed specifically
for medical datasets, HCS can be applied to a broad range of
data types, making it suitable for many domains. We describe the
details of the algorithm, and test its effectiveness on five
real-world datasets.