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On integrating clustering and statistical analysis for supporting cardiovascular disease diagnosis

Statistical analysis of medical data plays significant role in medical diagnostics development. However in many cases the statistics is not effective enough. In the paper we consider combining statistical inference with clustering in the preprocessing phase of data analysis. The proposed methodology is checked on cardiovascular data and used for developing methods of early diagnosis of hypertension in children. Experiments, conducted on the real data, have demonstrated that the proposed hybrid approach allowed to discover relationships which have not been identified by using only the statistical methods. We have observed approximately 30% growth in the number of correlations between diagnosed attributes. Moreover all the obtained statistically significant dependencies were stronger in clusters rather than in the whole datasets.