An improved ANN-based sequential global-local approximation for small medical data analysis




small data approach, non-iterative training, global-local approximation, GRNN, machine learning, body fat prediction task


INTRODUCTION: The task of approximation of complex nonlinear dependencies, especially in the case of short datasets, is important in various applied fields of medicine. Global approximation methods describe the generalized behavior of the model, while local methods explain the behavior of the model at specific data points. Global-local approximation combines both approaches, which makes such methods a powerful tool for processing short sets of medical data that can have both broad trends and local variations.

OBJECTIVES: This paper aims to improve the method of sequential obtaining global and local components of the response surface to increase the accuracy of prediction in the case of short sets of medical data.

METHODS: In this paper, the authors developed a new method that combined two ANNs: a non-iterative SGTM neural-like structure for obtaining the global component and GRNN as a powerful tool of local approximation in the case of short datasets.

RESULTS: The authors have improved the method of global-local approximation due to the use of a General Regression Neural Network instead of RBF ANN for obtaining the local component, which ensured an increase in the accuracy of the body fat prediction task. The authors optimized the operation of the method and investigated the efficiency of the sequential obtaining global and local components of the response surface in comparison with the efficiency using a number of existing methods.

CONCLUSION: The conducted experimental studies for solving the body fat prediction task showed the high efficiency of using the improved method in comparison with a number of existing methods, including ensemble methods.


Download data is not yet available.

Author Biographies

Dr Ivan Izonin, Lviv Polytechnic National University

Associated Professor at the Department of Artificial Intelligence, Lviv Polytechnic National University, Lviv, Ukraine 

Prof. Roman Tkachenko, Lviv Polytechnic National University

Professor at the Department of Publishing Information Technologies, Lviv Polytechnic National University, Lviv, Ukraine 

Roman Bliakhar, Lviv Polytechnic National University

PhD student the the Department of Artificial Intelligence, Lviv Polytechnic National University, Lviv, Ukraine 

Prof. Michal Kovac, Slovak University of Technology in Bratislava

Professor at the Faculty of Informatics and Information Technologies, Slovak University of Technology, Bratislava, Slovak Republic

Prof. Yevgeniy Bodyanskiy, Institute of Theological Studies of Our Lady of Immaculate Conception

Scientific Head, Dr. Sci., Prof.; Professor at the Deapartment, Dr. Sci., Prof. (Control Systems Research Laboratory; Department of Artificial Intelligence )

Olha Chala , Kharkiv National University of Radio Electronics

Researcher at the Control Systems Research Laboratory, Kharkiv National University of Radio Electronics, Kharkiv, Ukraine


Fan Z, Chiong R, Hu Z, Keivanian F, Chiong F. Body fat prediction through feature extraction based on anthropometric and laboratory measurements. Huk M, editor. PLoS ONE. 2022 Feb 22;17(2):e0263333.

Berezsky O, Zarichnyi M, Pitsun O. Development of a metric and the methods for quantitative estimation of the segmentation of biomedical images. EEJET. 2017 Dec 25;6(4 (90)):4–11. DOI:

Babenko V, Panchyshyn A, Zomchak L, Nehrey M, Artym-Drohomyretska Z, Lahotskyi T. Classical Machine Learning Methods in Economics Research: Macro and Micro Level Examples. WSEAS TRANSACTIONS ON BUSINESS AND ECONOMICS. 2021 Jan 5;18:209–17. DOI:

Babichev S, Lytvynenko V, Škvor J, Korobchynskyi M, Voronenko M. Information Technology of Gene Expression Profiles Processing for Purpose of Gene Regulatory Networks Reconstruction. In: 2018 IEEE Second International Conference on Data Stream Mining Processing (DSMP). 2018. p. 336–41. DOI:

Mochurad L. Optimization of Regression Analysis by Conducting Parallel Calculations. 2021;2870:982–96.

Stupnytskyi M, Zhukov V, Gorbach T, Biletskii O, Kutucu H. Analysis of the Early Posttraumatic Period Pathophysiology in Case of the Severe Combined Thoracic Trauma Using Multivariate Logistic Regression. 2019;2488:330–9.

Geche F, Mitsa O, Mulesa O, Horvat P. Synthesis of a Two Cascade Neural Network for Time Series Forecasting. In: 2022 IEEE 3rd International Conference on System Analysis & Intelligent Computing (SAIC) [Internet]. Kyiv, Ukraine: IEEE; 2022 [cited 2023 Feb 13]. p. 1–5. Available from: DOI:

Grzymała-Busse JW, Rza̧sa W. Local and Global Approximations for Incomplete Data. In: Peters JF, Skowron A, editors. Transactions on Rough Sets VIII [Internet]. Berlin, Heidelberg: Springer Berlin Heidelberg; 2008 [cited 2023 May 6]. p. 21–34. (Lecture Notes in Computer Science; vol. 5084). Available from: DOI:

Bisikalo O, Kharchenko V, Kovtun V, Krak I, Pavlov S. Parameterization of the Stochastic Model for Evaluating Variable Small Data in the Shannon Entropy Basis. Entropy. 2023 Jan 17;25(2):184. DOI:

Krak I, Barmak O, Manziuk E. Using visual analytics to develop human and machine‐centric models: A review of approaches and proposed information technology. Computational Intelligence. 2020 Feb 18;coin.12289. DOI:

Salazar A, Vergara L, Safont G. Generative Adversarial Networks and Markov Random Fields for oversampling very small training sets. Expert Systems with Applications. 2021 Jan 1;163:113819. DOI:

Costa L da F. A Global Approach to Local Approximations [Internet]. 2023 [cited 2023 May 6]. Available from:

Haftka RT. Combining global and local approximations. AIAA Journal. 1991 Sep;29(9):1523–5. DOI:

Bartels S. Combination of Global and Local Approximation Schemes for Harmonic Maps into Spheres. Journal of Computational Mathematics. 2009;27(2–3):170–83.

Van Der Smagt P, Groen F. Approximation with neural networks: between local and global approximation. In: Proceedings of ICNN 95 - International Conference on Neural Networks ICNN-95 [Internet]. Perth, WA, Australia: IEEE; 1995 [cited 2023 May 6]. p. 1060–4 vol.2. Available from: DOI:

Wedge D, Ingram D, Mclean D, Mingham C, Bandar Z. On Global–Local Artificial Neural Networks for Function Approximation. IEEE Trans Neural Netw. 2006 Jul;17(4):942–52. DOI:

Tkachenko R, Doroshenko A, Izonin I, Tsymbal Y, Havrysh B. Imbalance Data Classification via Neural-Like Structures of Geometric Transformations Model: Local and Global Approaches. In: Hu Z, Petoukhov S, Dychka I, He M, editors. Advances in Computer Science for Engineering and Education [Internet]. Cham: Springer International Publishing; 2019 [cited 2023 May 1]. p. 112–22. (Advances in Intelligent Systems and Computing; vol. 754). Available from: DOI:

Herrera LJ, Pomares H, Rojas I, Guillén A, Rubio G, Urquiza J. Global and local modelling in RBF networks. Neurocomputing. 2011 Sep;74(16):2594–602. DOI:

Medykovskvi M, Pavliuk O, Sydorenko R. Use of Machine Learning Technologys for the Electric Consumption Forecast. In: 2018 IEEE 13th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT). 2018. p. 432–5. DOI:

Subbotin S. Radial-Basis Function Neural Network Synthesis on the Basis of Decision Tree. Opt Mem Neural Networks. 2020 Jan 1;29(1):7–18. DOI:

Chumachenko D, Chumachenko T, Meniailov I, Pyrohov P, Kuzin I, Rodyna R. On-Line Data Processing, Simulation and Forecasting of the Coronavirus Disease (COVID-19) Propagation in Ukraine Based on Machine Learning Approach. In: Data Stream Mining & Processing [Internet]. Springer, Cham; 2020 [cited 2021 Mar 25]. p. 372–82. Available from: DOI:

Tkachenko R. An Integral Software Solution of the SGTM Neural-Like Structures Implementation for Solving Different Data Mining Tasks. In: Babichev S, Lytvynenko V, editors. Lecture Notes in Computational Intelligence and Decision Making. Cham: Springer International Publishing; 2022. p. 696–713. (Lecture Notes on Data Engineering and Communications Technologies). DOI:

Kotsovsky V, Batyuk A, Yurchenko M. New Approaches in the Learning of Complex-Valued Neural Networks. In: 2020 IEEE Third International Conference on Data Stream Mining Processing (DSMP). 2020. p. 50–4. DOI:

Teslyuk V, Kazarian A, Kryvinska N, Tsmots I. Optimal Artificial Neural Network Type Selection Method for Usage in Smart House Systems. Sensors. 2021 Jan;21(1):47. DOI:

Izonin I, Kryvinska N, Vitynskyi P, Tkachenko R, Zub K. GRNN Approach Towards Missing Data Recovery Between IoT Systems. In: Barolli L, Nishino H, Miwa H, editors. Advances in Intelligent Networking and Collaborative Systems. Cham: Springer International Publishing; 2020. p. 445–53. (Advances in Intelligent Systems and Computing). DOI:

Tolstyak Y, Havryliuk M. An Assessment of the Transplant’s Survival Level for Recipients after Kidney Transplantations using Cox Proportional-Hazards Model. 2022;3302:260–5.

Tolstyak Y, Chopyak V, Havryliuk M. An investigation of the primary immunosuppressive therapy’s influence on kidney transplant survival at one month after transplantation. Transplant Immunology. 2023 Jun;78:101832. DOI:

Roger Johnson. Body fat dataset [Internet]. StatLib. [cited 2023 May 6]. Available from:

Body fat prediction through feature extraction based on anthropometric and laboratory measurements. PLOS ONE. 2022 Feb 22;17(2):e0263333. DOI:

Di Caprio D, Ebrahimnejad A, Alrezaamiri H, Santos-Arteaga FJ. A novel ant colony algorithm for solving shortest path problems with fuzzy arc weights. Alexandria Engineering Journal. 2022 May;61(5):3403–15. DOI:

Sumalatha M, Parthiban L. Augmentation of Predictive Competence of Non-Small Cell Lung Cancer Datasets through Feature Pre-Processing Techniques. EAI Endorsed Trans Perv Health Tech. 2022 Nov 2;8(5):e1. DOI:

Oleksiv I, Mirzoieva D. Impact of Remittances on the Exchange Rate and Consumption: Evidence from Ukraine. Eastern European Economics. 2022 Sep 3;60(5):418–32. DOI:

Oleksiv I, Kharchuk V, Shulyar R, Dluhopolskyi O. Quality of Student Support at IT Educational Programmes: Case of Lviv Polytechnic National University. In: 2021 11th International Conference on Advanced Computer Information Technologies (ACIT) [Internet]. Deggendorf, Germany: IEEE; 2021 [cited 2023 Jun 3]. p. 270–5. Available from: DOI:




How to Cite

Izonin I, Tkachenko R, Bliakhar R, Kovac M, Bodyanskiy Y, Chala O. An improved ANN-based sequential global-local approximation for small medical data analysis. EAI Endorsed Trans Perv Health Tech [Internet]. 2023 Sep. 26 [cited 2023 Dec. 10];9. Available from: