The paper describes the medical data personalization problem by determining the individual characteristics needed to predict the number of days a patient spends in a hospital. The mathematical problem of patient information analysis is formalized, which will help identify critical personal characteristics based on conditioned space analysis. The condition space is given in cube form as a reflection of the functional relationship of the general parameters to the studied object. The dataset consists of 51 instances, and ten parameters are processed using different clustering and regression models. Days in hospital is the target variable. A condition space cube is formed based on clustering analysis and features selection. In this manner, a hierarchical predictor based on clustering and an ensemble of weak regressors is built. The quality of the developed hierarchical predictor for Root Mean Squared Error metric is 1.47 times better than the best weak predictor (perceptron with 12 units in a single hidden layer).
Finding dependencies in the data requires the analysis of relations between dozens of parameters of the studied process and hundreds of possible sources of influence on this process. Dependencies are nondeterministic and therefore modeling requires the use of statistical methods for analyzing random processes. Part of the information is often hidden from observation or not monitored. That is why many difficulties have arisen in the process of analyzing the collected information. The paper aims to find frequent patterns and parameters affected by COVID-19. The novelty of the paper is hierarchical architecture comprises supervised and unsupervised methods. It allows the development of an ensemble of the methods based on k-means clustering and classification. The best classifiers from the ensemble are random forest with 500 trees and XGBoost. Classification for separated clusters gives us higher accuracy on 4% in comparison with dataset analysis. The proposed approach can be used also for personalized medicine decision support in other domains. The features selection allows us to analyze the following features with the highest impact on COVID-19: age, sex, blood group, had influenza.
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