Models of Latent Predictors in Intellectual Systems for Forecasting the State of Living Systems
Purpose of research. The purpose of the study is to improve the quality of forecasting the functional state of living systems by forming models of additional predictors in intelligent forecasting systems with heterogeneous decision modules. Methods. To predict the functional state of living systems, it is proposed to supplement the heterogeneous decision modules with virtual flows. The virtual flow is represented by a latent predictor model that reflects hidden systemic links between real and latent predictors. As a result, the vector of informative features consists of two subvectors. One of them corresponds to real flows, the second to virtual flows (latent variables). To form a virtual stream, a feedback loop is used, which includes a series-connected subtractor, comparator, adder, and control unit. An additional informative feature that takes into account hidden systemic connections between the components of the space of informative features that are fed to the inputs of heterogeneous classifiers is formed at the output of the adder. Results. To synthesize a latent predictor model, an algorithm and supporting software have been developed. The synthesis of the latent predictor model is performed in six steps. Using the algorithm, it is possible to generate a new training set, the dimension of the informative vectors of the samples of which exceeds by one component the dimension of the vectors of the samples of the original training set. This makes it possible to introduce an additional trained neural network into the intelligent system and use it as a virtual flow model. The testing of the software of an intelligent system with virtual flows was carried out for the tasks of classifying the risk of myocardial reinfarction. Conclusion. ROC analysis of the quality of decision-making by heterogeneous classifiers for assessing the risk of myocardial infarction in representative control samples showed that when using models of latent predictors in heterogeneous classifiers, diagnostic sensitivity and diagnostic specificity increase by 5 ... 8%.