Abstract
The paper describes the optimization of the prediction of disease at the primary health care level with a complex phased application of information techniques. The approach is based on analysis of the average values of indicators, correlation coefficients, using multi-parameter neural network clustering, ROC-analysis and decision tree.The data of 63 patients with arterial hypertension obtained at teaching and practical centers of primary health care were used for the analysis. It has been established that neural network clasterization can effectively and objectively allocate patients into the appropriate categories according to the level of average indices of patient examination results. Determination of the sensitivity and specificity of hemodynamic parameters, including blood pressure, and repeated during the initial survey was conducted using ROC-analysis.The diagnostic criteria of decision-making were developed to optimize the prediction of disease at the primary level in order to adjust examination procedures and treatment based on the analysis of indicators of patient examination with a complex gradual application of information procedures.
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