INFORMATION SUPPORT SYSTEM OF MEDICAL SYSTEM RESEARCH

Abstract

Background. Medical system research requires information support system of implementing data mining algorithms resulting in decision trees or IF-THEN rules. Besides that, this system should be object-oriented and web-integrated. Objective. The aim of this study was to develop information support system based on data mining algorithms applied to system analysis method for medical system research. Methods. System analysis methods are used for qualitative analysis of mathematical models diseases. Algorithms such as decision tree induction and sequential covering algorithm are applied for data mining from learning data set. Results. Taking into consideration the complexity of mathematical equations (nonlinear systems with delays), scientific community requires the appearance of new powerfull methods of exact parameter identification and qualitative analysis. From the point of view of theoretical medicine, uncertainties arising in models of diseases require to develop treatment schemes that are effective, take into account toxicity constraints, enable better life quality, have cost benefit. Multivariate method of qualitative analysis of mathematical models can be used for pathologic process forms of classification. Conclusions. The complex qualitative behavior of diseases models depending on parameters and controllers was observed in our investigation even without considering probabilistic nature of the majority of quantities and parameters of information models. KEY WORDS: data mining, system analysis, medical research, decision making