Risk prediction system for pharmacological problems

This work considers the results of laboratory investigations carried out to create a system for predicting cardiac necrosisrisks that would be based on algorithms and procedures of data mining. Continuous data that indicated changes in the heartbeatand descriptive characteristics of the test animals were used. The procedures of data mining used included the selection of attributes, preprocessing, clusterization, classification, forecasting, and the data analysis. The belonging of an object to a particular group is found out during the clusterization and preprocessing of continuous data. Correlation among different descriptive characteristics of the animals is determined. The correlation between the continuous data and descriptive characteristics is found using a classification whose results are integrated in the form of conditional rules with the evaluation of the cardiac necrosisrisks obtained in the laboratory. The resulted conditional rules and descriptive characteristics of the test animals provide the basis for predicting the cardiac necrosisrisks.