MATHEMATICAL DECODING OF THE CORRELATION BETWEEN DIFFERENT ORGANS’ ACTIVITIES: A REVIEW

Jamaluddin Abdullah, Vladimir Kasik, Penhaker Marek, Ondrej Krejcar, Arvind Panwar and Hamidreza Namazi
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Jamaluddin Abdullah: School of Mechanical Engineering, Universiti Sains Malaysia, Malaysia
Vladimir Kasik: Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science (FEEC), VSB–Technical University of Ostrava, 17. listopadu 2172/15, Ostrava, Czechia
Penhaker Marek: Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science (FEEC), VSB–Technical University of Ostrava, 17. listopadu 2172/15, Ostrava, Czechia
Ondrej Krejcar: Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science (FEEC), VSB–Technical University of Ostrava, 17. listopadu 2172/15, Ostrava, Czechia3Center for Basic and Applied Research, Faculty of Informatics and Management, University of Hradec Kralove, Hradec Kralove, Czechia
Arvind Panwar: Department of Cybersecurity, School of Computing Science and Engineering, Galgotias University, Greater Noida, Uttar Pradesh 203201, India
Hamidreza Namazi: Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science (FEEC), VSB–Technical University of Ostrava, 17. listopadu 2172/15, Ostrava, Czechia5School of Engineering, Monash University, Selangor, Malaysia6Department of Informatics, Faculty of Science, University of South Bohemia in České Budějovice, Czechia7Biomedical Signal and Image Processing Lab, Galgotias University, Greater Noida, Uttar Pradesh, India8Faculty of Engineering and the Built Environment, Durban University of Technology, Durban, South Africa

FRACTALS (fractals), 2025, vol. 33, issue 09, 1-14

Abstract: Understanding the correlation between different organ activities is essential for advancing knowledge in physiology, medicine, and bioengineering. While existing literature often focuses on individual organs, a significant gap remains in synthesizing the diverse mathematical methodologies used to decode complex multi-organ relationships. This review addresses that gap by providing a comprehensive analysis of advanced mathematical tools — including time series analysis, signal processing, entropy measures, fractal theory, network modeling, and machine learning (ML) — that have been applied to characterize dynamic inter-organ communication. We discuss how these methods reveal nonlinear, causal, and scale-invariant relationships among organ systems and how they are used to predict pathological conditions, monitor health status, and inform personalized interventions. By bridging theoretical models with clinical applications, this review offers a unified framework for understanding systemic physiology and supports future advancements in multi-organ diagnostics and therapies.

Keywords: Clinical Diagnosis; Nonlinear Biomarkers; Multi-Organ Correlation; Entropy Analysis; Signal Complexity; Physiological Modeling (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1142/S0218348X25300120

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