Exploring relationships between genotype and oral cancer development through XCS

In medical research, being able to justify decisions is generally as important as taking the right ones. Interpretability is then one of the chief characteristics a learning algorithm must have, in order to be successfully applied to a medical data set. Other important features are seamless treatment of different data types, and ability to cope well with missing values. XCS and decision trees both appear to have this desirable characteristics; we compared them on a data set regarding Head and neck squamous cell carcinoma (HNSCC). This kind of oral cancer already been found to be associated with smoking and alcohol drinking habits. However the individual risk could be modified by genetic polymorphisms of enzymes involved in the metabolism of tobacco carcinogens and in the DNA repair mechanisms. To study this relationship, the data set comprised demographic and life-style (age, gender, smoke and alcohol), and genetic data (the individual genotype of 11 polymorphic genes), with the inf...

• Type: Conference
• Year: 2005
• Where: GECCO
• Authors: Alessandro Passaro, Flavio Baronti, Valentina Maggini
• Keywords: Data Set, Decision Trees, GECCO 2005, Genetic Data