Personalized Data Analysis Approach for Assessing Necessary Hospital Bed-Days Built on Condition Space and Hierarchical Predictor
| Autor: | Kateryna Melnykova, Khrystyna Lishchuk-Yakymovych, Nataliya Shakhovska, Nataliia Melnykova, Volodymyr Melnykov |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Technology
Mean squared error Computer science data analysis Regression analysis Perceptron computer.software_genre health care Computer Science Applications Management Information Systems Personalization Variable (computer science) prediction task machine learning Artificial Intelligence hierarchical predictor Metric (mathematics) medical insurance Data mining Cube Cluster analysis computer personalization Information Systems |
| Zdroj: | Big Data and Cognitive Computing, Vol 5, Iss 37, p 37 (2021) Big Data and Cognitive Computing Volume 5 Issue 3 |
| ISSN: | 2504-2289 |
| Popis: | 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). |
| Databáze: | OpenAIRE |
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