Machine learning disciplines including model design and data preprocessing are crucial in order to obtain a good performance in terms of accurate results and interpretability. However, they are not usually treated simultaneously, and, when a model is evaluated, the origin and preprocessing of the data is ignored. Medicine and biomedical research provide a wide variety of problems where machine learning can be very helpful in decision support, telemedicine, and the discovery of interactions. Among these, it is possible to find variable selection, classification, regression, image processing, and so on.

Therefore, this special issue is focused on methods and applications where machine learning could be applied holistically encompassing all stages to solve the problem. It is also interesting to have comparative analysis of the wide variety of theoretical models when applied to a concrete problem with specific characteristics. Potential topics include, but are not limited to:

• New problems in medicine mapped with machine learning disciplines
• Data preprocessing considering
  • Variable selection and risk factor identifications
  • Treatment of unbalanced datasets considering the specificity and sensibility of the diagnosis
  • Prototype/instance selection for removing noise and decreasing the datasets
• Theoretical models comparison for specific problems
  • Classification of biometrics and disease
  • Accuracy in the regression model
  • Interpretability of the model to help decision support

Before submission authors should carefully read over the journal's Author Guidelines, which are located at http://www.hindawi.com/journals/cmmm/guidelines/. Prospective authors should submit an electronic copy of their complete manuscript through the journal Manuscript Tracking System at http://mts.hindawi.com/submit/journals/cmmm/mdm/ according to the following timetable: