Affiliations: [a] Department of Computer Architecture, Universidad Politécnica de Madrid, Madrid, Spain | [b] National Research Council of Canada, Ottawa, Canada | [x] Universidad Politécnica de Madrid, Spain | [y] Sobolev Institute of Mathematics, Russia
Abstract: Traditionally, clinical data have been used as the only source of information to diagnose diseases. Nowadays, other types of information, such as various forms of omics data (e.g. DNA microarrays), are taken into account to improve diagnosis and even prognosis in many diseases. This paper proposes a new approach, called CliDaPa, for efficiently combining both sources of information, namely clinical data and gene expressions, in order to further improve estimations. In this approach, patients are firstly divided into different clusters (represented as a decision tree) depending on their clinical information. Thus, different groups of patients with similar behaviors are identified. Each individual group can be studied and classified separately, using only gene expression data, with different supervised classification methods, such as decision trees, Bayesian networks or lazy induction learning. To validate this method, two datasets based on Breast Cancer, a high social impact disease, have been used. For the proposed approach, internal (0.632 Bootstrap) and external validations have been carried out. Results have shown improvements in accuracy in the internal and external validation compared with the standard methods with clinical data and gene expression data separately. Thus, the CliDaPa algorithm fulfills our proposed objectives.
Keywords: Breast cancer, DNA microarray, clinical, data mining, clinical tree
