Neutrosophic method for the recommendation in the identification of growth factors and their relationship in periodontics

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Cristian Vicente Morocho Segarra
Adriana Nicole Tobar Peñaherrera
Daniel Gustavo Cortés Naranjo

Abstract

Growth factors are biochemical signals that exert various effects on the body's cells and are not limited to regulating cell proliferation and differentiation. They can cause cell death and participate in repair and regeneration processes, being considered initiators of healing processes. The present research aims to develop a neutrosophic method for recommendation in the identification of growth factors and their relationship in periodontics. Periodontology faces the challenge of healing and regenerating complex structures that involve both cells and extracellular matrix. Growth factors are essential in this process, as they regulate cellular interaction and promote wound healing, thus contributing to the restoration of tissues affected by periodontal diseases. The neutrosophic approach makes it possible to manage the uncertainty and imprecision associated with the identification of these factors, providing a more flexible and adaptive framework than traditional methods. Through this approach, it was possible to generate personalized recommendations that consider not only the nature of growth factors, but also their interrelationship with different clinical and biological conditions. Recommendations generated by this method are highly useful for dental health professionals, as they allow for better decision-making in personalized periodontal treatments. By integrating information on the efficacy of growth factors in different situations, professionals receive recommendations that allow them to optimize therapeutic approaches, thus improving the results in periodontal tissue regeneration.

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Neutrosophic method for the recommendation in the identification of growth factors and their relationship in periodontics. (2024). Neutrosophic Computing and Machine Learning. ISSN 2574-1101, 35, 339-346. http://fs.unm.edu/NCML2/index.php/112/article/view/645
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How to Cite

Neutrosophic method for the recommendation in the identification of growth factors and their relationship in periodontics. (2024). Neutrosophic Computing and Machine Learning. ISSN 2574-1101, 35, 339-346. http://fs.unm.edu/NCML2/index.php/112/article/view/645