Objectives: The aim of our work was to implement a prototype of a decision support system which has the form of a web-based classification service. Because the data analysis component of decision support systems often happens to be unsuitable for high-dimensional data, special attention must be paid to the sophisticated selection of the most relevant variables before learning the classification rule.
Methods: We implemented a prototype of a diagnostic decision support system called SIR. The system has the ability to select the most relevant variables based on a set of high-dimensional measurements by means of a forward procedure optimizing a decision-making criterion. This allows to learn a reliable classification rule.
Results: The implemented prototype was tested on a sample of patients involved in a cardiology study. We used SIR to perform an information extraction from a cardiological clinical study containing both clinical and gene expression data. The classification performance was evaluated by means of a cross validation study.
Conclusions: The proposed classification system can be useful for clinicians in primary care to support their decision-making tasks with relevant information extracted from any available clinical study. It is especially suitable for analyzing high-dimensional data, e.g. gene expression measurements.
Haberler
Haberler
Haberler
Haberler
Haberler
Haberler
Haberler
Haberler
Haberler
Haberler
Haberler
Haberler
Haberler
Haberler
Haberler
Haberler
Haberler
Haberler
Haberler
Haberler
Haberler
Haberler
Haberler
Haberler
Haberler
Haberler
Haberler
Haberler
Haberler
Haberler
Haberler
Haberler
Haberler
Haberler
Haberler
Haberler
Haberler
Haberler
Haberler
Haberler
Haberler
Haberler
Haberler
Haberler
Haberler
Haberler
Haberler
Haberler
Haberler
Haberler