Machine Learning
An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Preventing "Overfitting" of Cross-Validation Data
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Cancer classification and prediction using logistic regression with Bayesian gene selection
Journal of Biomedical Informatics - Special issue: Biomedical machine learning
Integrating gene expression profiling and clinical data
International Journal of Approximate Reasoning
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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.