An interpretable ensemble structure with a non-iterative training algorithm to improve the predictive accuracy of healthcare data analysis
Ivan Izonin,
Roman Tkachenko,
Kyrylo Yemets
et al.
Abstract:The modern development of healthcare is characterized by a set of large volumes of tabular data for monitoring and diagnosing the patient's condition. In addition, modern methods of data engineering allow the synthesizing of a large number of features from an image or signals, which are presented in tabular form. The possibility of high-precision and high-speed processing of such large volumes of medical data requires the use of artificial intelligence tools. A linear machine learning model cannot accurately a… Show more
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