A non-linear SVR-based cascade model for improving prediction accuracy of biomedical data analysis

Ivan Izonin; Roman Tkachenko; Olexander Gurbych; Michal Kovac; Leszek Rutkowski; Rostyslav Holoven; Ivan Izonin; Roman Tkachenko; Olexander Gurbych; Michal Kovac; Leszek Rutkowski; Rostyslav Holoven

doi:10.3934/mbe.2023597

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 7: 13398-13414. doi: 10.3934/mbe.2023597

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A non-linear SVR-based cascade model for improving prediction accuracy of biomedical data analysis

1.
Department of Artificial Intelligence, Institute of Computer Sciences and Information Technologies, Lviv Polytechnic National University, Lviv, Ukraine
2.
Department of Publishing Information Technologies, Institute of Computer Sciences and Information Technologies, Lviv Polytechnic National University, Lviv, Ukraine
3.
Faculty of Informatics and Information Technologies, Slovak University of Technology, Bratislava, Slovak Republic
4.
Systems Research Institute of the Polish Academy of Sciences, Warsaw, Poland
5.
AGH University of Science and Technology, Krakow, Poland
6.
Information Technology Institute, University of Social Sciences, Lodz, Poland
7.
Department of System Design, Ivan Franko National University of Lviv, Lviv, Ukraine

Academic Editor: Timon Rabczuk

Received: 17 April 2023 Revised: 16 May 2023 Accepted: 22 May 2023 Published: 12 June 2023

Biomedical data analysis is essential in current diagnosis, treatment, and patient condition monitoring. The large volumes of data that characterize this area require simple but accurate and fast methods of intellectual analysis to improve the level of medical services. Existing machine learning (ML) methods require many resources (time, memory, energy) when processing large datasets. Or they demonstrate a level of accuracy that is insufficient for solving a specific application task. In this paper, we developed a new ensemble model of increased accuracy for solving approximation problems of large biomedical data sets. The model is based on cascading of the ML methods and response surface linearization principles. In addition, we used Ito decomposition as a means of nonlinearly expanding the inputs at each level of the model. As weak learners, Support Vector Regression (SVR) with linear kernel was used due to many significant advantages demonstrated by this method among the existing ones. The training and application procedures of the developed SVR-based cascade model are described, and a flow chart of its implementation is presented. The modeling was carried out on a real-world tabular set of biomedical data of a large volume. The task of predicting the heart rate of individuals was solved, which provides the possibility of determining the level of human stress, and is an essential indicator in various applied fields. The optimal parameters of the SVR-based cascade model operating were selected experimentally. The authors shown that the developed model provides more than 20 times higher accuracy (according to Mean Squared Error (MSE)), as well as a significant reduction in the duration of the training procedure compared to the existing method, which provided the highest accuracy of work among those considered.
- cascading,
- data analysis,
- biomedical data,
- Ito decomposition,
- ensemble model,
- linear Support Vector Machine,
- non-linear input extension,
- prediction tasks
Citation: Ivan Izonin, Roman Tkachenko, Olexander Gurbych, Michal Kovac, Leszek Rutkowski, Rostyslav Holoven. A non-linear SVR-based cascade model for improving prediction accuracy of biomedical data analysis[J]. Mathematical Biosciences and Engineering, 2023, 20(7): 13398-13414. doi: 10.3934/mbe.2023597

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Abstract

Biomedical data analysis is essential in current diagnosis, treatment, and patient condition monitoring. The large volumes of data that characterize this area require simple but accurate and fast methods of intellectual analysis to improve the level of medical services. Existing machine learning (ML) methods require many resources (time, memory, energy) when processing large datasets. Or they demonstrate a level of accuracy that is insufficient for solving a specific application task. In this paper, we developed a new ensemble model of increased accuracy for solving approximation problems of large biomedical data sets. The model is based on cascading of the ML methods and response surface linearization principles. In addition, we used Ito decomposition as a means of nonlinearly expanding the inputs at each level of the model. As weak learners, Support Vector Regression (SVR) with linear kernel was used due to many significant advantages demonstrated by this method among the existing ones. The training and application procedures of the developed SVR-based cascade model are described, and a flow chart of its implementation is presented. The modeling was carried out on a real-world tabular set of biomedical data of a large volume. The task of predicting the heart rate of individuals was solved, which provides the possibility of determining the level of human stress, and is an essential indicator in various applied fields. The optimal parameters of the SVR-based cascade model operating were selected experimentally. The authors shown that the developed model provides more than 20 times higher accuracy (according to Mean Squared Error (MSE)), as well as a significant reduction in the duration of the training procedure compared to the existing method, which provided the highest accuracy of work among those considered.

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