MATHEMATICAL MODELING OF ENDOCRINE DISEASE PREVENTION AND EARLY DETECTION PROCESSES
Received: 2025-05-20
Published: 2026-06-06
Abstract
This study examines the prevention and early diagnosis of endocrine diseases on the basis of mathematical models. Probability theory, statistical analysis, classification, and clustering methods were applied to optimize the diagnostic process. The results show that mathematical models improve diagnostic accuracy and enable efficient use of healthcare resources. Using Bayes’ theorem, disease probability was calculated, risk groups were identified, and disease progression was predicted. Clustering based on symptoms contributes to more accurate diagnosis. Systematic analysis, in turn, improves the effectiveness of prevention and enables the development of an individualized treatment plan.
Keywords
List of references
-
Garrison, F. H., & Morton, R. S. (2018). Medical History. Endocrine System Disorders. London. Elsevier Publishing.
-
Jameson, J. L., & De Groot, L. J. (2020). Endocrinology. Adult and Pediatric. Philadelphia. Saunders.
-
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). (2021). "Advances in Endocrine Research." Bethesda. National Institutes of Health.
-
American Diabetes Association. (2021). "Standards of Medical Care in Diabetes—2021." Diabetes Care, 44(Supplement 1), S1–S232.
-
Cooper, D. S., & Biondi, B. (2019). "Subclinical Thyroid Disease." The Lancet, 379(9821), 1142–1154.
-
Gharib, H., Papini, E., & Garber, J. R. (2019). "AACE/ACE/AME Task Force on Thyroid Nodules. Management Guidelines." Endocrine Practice, 22(5), 622–639.
-
Graves, L., & Rees, A. (2018). "Diagnosis and Management of Endocrine Disorders in Pregnancy." Obstetrics, Gynaecology & Reproductive Medicine, 28(4), 114–122. Nussey, S., & Whitehead, S. (2019). Endocrinology. An Integrated Approach (2nd ed.). Oxford. Wiley-Blackwell.
-
Nishanov, A., Mamajanov , R., Xaydarov , S., Mengturayev , F., & Yuldashev , R. (2024). sut bezi saraton kasalliklarini simptomlarini mashinali o‘qitishga tayyorlash bosqichlari. digital transformation and artificial intelligence, 2(6), 237–249. Retrieved from https://dtai.tsue.uz/index.php/dtai/article/view/v2i633
-
Nishanov, A., Mengturayev, F., Allayarov, U., & Xaydarov, S. (2024). endokrin kasalliklarini tashxislashda foydalaniladigan simptomlarni shakllantirish bosqichlari. digital transformation and artificial intelligence, 2(6), 228–236. Retrieved from https://dtai.tsue.uz/index.php/dtai/article/view/v2i632
-
Nishanov, A. H., Akbaraliev, B. B., & Tajibaev, S. K. (2020, October). About one feature selection algorithm in pattern recognition. In World Conference Intelligent System for Industrial Automation (pp. 103-112). Cham. Springer International Publishing.
-
Nishanov, A. K., Turakulov, K. A., & Turakhanov, K. V. (1999). A decision rule for identification of eye pathologies. Biomedical Engineering, 33, 178-179.
-
Nishanov, А. H., & Samandarov, B. S. (2015). Assessment model of monitoring and defining the completeness of course elements of information systems. Europaische Fachhochschule, (5), 56-58.
-
Nishanov, A. K., Allamov, O. T., Ruzibaev, O. B., Abdullaev, A. S., & Allamova, S. T. (2021, November). An approach to finding the most optimal route in a dynamic graph. In 2021 International Conference on Information Science and Communications Technologies (ICISCT) (pp. 01-05). IEEE.
-
Atoev, S., Nishanov, A., & Abdirazakov, F. (2021, November). Object Tracking Method Based on Kalman Filter and Camshift Algorithm for UAV Applications. In 2021 International Conference on Information Science and Communications Technologies (ICISCT) (pp. 1-4). IEEE.
-
Rakhmanov, A. T., Nishanov, A. K., Ruzibaev, O. B., & Shaazizova, M. E. (2020, November). On one method for solving the multi-class classification problem. In 2020 International Conference on Information Science and Communications Technologies (ICISCT) (pp. 1-4). IEEE.
About the Authors
License
This work is licensed under a Creative Commons Attribution 4.0 International License.