SUT BEZI SARATONI DIAGNOSTIKASINING MATEMATIK MODELLARI
Ключевые слова:
mashina o‘qitish, kasallikni bashorat qilish, sun’iy intellekt, diagnostika modellashtirish.Аннотация
В исследовании рассматривался вопрос раннего выявления рака молочной железы с использованием математических моделей. Математическая реализация математических моделей и методов машинного обучения в диагностике рака молочной железы в исследовании включает следующие этапы: сбор и предварительная обработка данных, формирование модели, анализ и классификация данных, точность и специфичность. Особое внимание уделено оценке производительности посредством измерения. В ходе исследования мы выполнили следующие задачи: математическое представление данных, формулы для статической диагностики, прогнозирование прогрессирования рака, логистическая регрессия, данная математическая модель представляет параметры, используемые для обнаружения и прогнозирования заболевания.
Библиографические ссылки
1. Siegel, R. L., Miller, K. D., & Jemal, A. (2020). Cancer statistics, 2020. CA: A Cancer Journal for Clinicians, 70(1), 7–30. https://doi.org/10.3322/caac.21590
2. Harbeck, N., Penault-Llorca, F., Cortes, J., et al. (2019). Breast cancer. Nature Reviews Disease Primers, 5(1), 66. https://doi.org/10.1038/s41572-019-0111-2
3. Woldemichael, A., & Worku, E. (2020). Machine learning algorithms for breast cancer prediction and diagnosis. Journal of Applied Health Sciences, 4(3), 145–156. https://doi.org/10.1016/j.jahs.2020.05.007
4. Giger, M. L. (2020). Machine learning in medical imaging: Breast cancer diagnosis and prognosis. Annual Review of Biomedical Engineering, 22(1), 147–171. https://doi.org/10.1146/annurev-bioeng-111219-090931
5. GLOBOCAN (2021). Breast cancer statistics 2021. International Agency for Research on Cancer. Retrieved from https://gco.iarc.fr).
6. Giger, M. L. (2020). Machine learning in medical imaging: Breast cancer diagnosis and prognosis. Annual Review of Biomedical Engineering, 22, 147–171. https://doi.org/10.1146/annurev-bioeng-111219-090931
7. Harbeck, N., Penault-Llorca, F., Cortes, J., Gnant, M., Houssami, N., Poortmans, P., ... & Cardoso, F. (2019). Breast cancer. Nature Reviews Disease Primers, 5(1), 66. https://doi.org/10.1038/s41572-019-0111-2
8. Atakhanova, N. E., Shayusupov, N. R., Ishakov, D. M., Kakhkharov, A. Zh., & Shodmanova, D. S. (2022). Sut bezi saratoni prognozining mammografik va ultratovushli prognostik omillari. Tashkent Medical Academy Journal, 86(1), 87–90.
9. Ahmadova, M. A. (2024). Sut bezi saratonida radiologik tasvir va morfologik xususiyatlarni qiyosiy tavsiflash. Journal of Healthcare and Life-Science Research, 3(6).
10. Ergashev, A. J., & Oralova, S. B. (2024). Ayollarda ko‘krak bezi saratoni diagnostikasi, belgilari va davolash usullari. Modern Education and Development, 15(4), 98–101.
11. Nishanova, Y. X., Ibroximova, F. J., & Musulmonov, Sh. R. (2023). Sut bezi saratoni skriningi. Science and Pedagogy in the Modern World, 1(1), 67–69.
12. Nishanova, Y. X., Ibroximova, F. J., & Musulmonov, Sh. R. (2023). Sut bezi saratoni skriningi. Zenodo.
13. Giger, M. L. (2020). Machine learning in medical imaging: Breast cancer diagnosis and prognosis. Annual Review of Biomedical Engineering, 22, 147–171.
14. Harbeck, N., Penault-Llorca, F., Cortes, J., Gnant, M., Houssami, N., Poortmans, P., ... & Cardoso, F. (2019). Breast cancer. Nature Reviews Disease Primers, 5(1), 66.
15. Bozorov, E. X., & Ergashev, A. J. (2022). Tibbiyotda magnit rezonans tomografiyasi mavzusni yangi pedagogik texnologiya asosida o‘qitish. Pedagogik Mahorat, 2, 222–227.
16. Alimkhodjaeva, L. T., & Norbekova, M. H. (2020). Clinical significance of the density of tumor microvessels in breast cancer in men. Central Asian Journal of Medicine, 2, 56–64.
17. Zakirova, L. T., & Alimkhodjaeva, L. T. (2018). Chromosomal disorders and aberrant DNA methylation as early biomarkers of breast cancer risk in young women. Journal of Life Science and Biomedicine, 8(1), 1–5.
18. Norbekova, M. H., Alimkhodjaeva, L. T., & Mirolimov, M. M. (2019). Vzglyad na problemu raka molochnoy zhelezy u muzhchin v Respublike Uzbekistan. Uzbekistan Medical Journal, 1, 56–64.
19. Gonzalez, R., Nejat, P., Saha, A., Campbell, C. J. V., Norgan, A. P., & Lokker, C. (2023). Performance of externally validated machine learning models based on histopathology images for the diagnosis, classification, prognosis, or treatment outcome prediction in female breast cancer: A systematic review. arXiv preprint arXiv:2312.06697.
20. Levshinskii, V., Polyakov, M., Losev, A., & Khoperskov, A. (2019). Verification and Validation of Computer Models for Diagnosing Breast Cancer Based on Machine Learning for Medical Data Analysis. arXiv preprint arXiv:1910.02779.
21. Stoia, S., Băciuț, G., Lenghel, M., & Badea, R. (2021). Imaging in the preoperative diagnosis of parotid gland tumors. Bosnian Journal of Basic Medical Sciences. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7861630/
22. Song, X., Yang, X., & Narayanan, R. (2020). Saliva metabolic profiling in oral squamous cell carcinoma diagnosis. Proceedings of the National Academy of Sciences. Retrieved from https://www.pnas.org/doi/pdf/10.1073/pnas.2001395117
23. Liu, S., Yu, B., Zheng, X., Guo, H., & Shi, L. (2024). Construction and application of a nomogram for predicting parotid tumors. Journal of Computer Assisted Tomography. Retrieved from https://journals.lww.com/jcat/fulltext/2024/01000/construction_and_application_of_a_nomogram_for.21.aspx
24. Prezioso, E., Izzo, S., & Giampaolo, F. (2021). Predictive medicine for salivary gland tumors using deep learning. IEEE Journal of Biomedical and Health Informatics. Retrieved from https://ieeexplore.ieee.org/abstract/document/9573315/
25. Lavareze, L., Scarini, J. F., & de Lima-Souza, R. A. (2022). Tumor microenvironment in salivary gland cancer: Translational routes for therapy. Critical Reviews in Oncology/Hematology. Retrieved from https://www.sciencedirect.com/science/article/pii/S1040842822000294
26. Chen, W., Zhu, L. N., Dai, Y. M., & Jiang, J. S. (2020). Differentiation of salivary gland tumors using diffusion-weighted imaging. The British Journal of Radiology. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7465841/
27. Lopez, J. E. (2022). Dose calculations for salivary glands in prostate cancer therapy. Georgetown University Repository. Retrieved from https://repository.library.georgetown.edu/bitstream/handle/10822/1082659/Lopez_georgetown_0076M_15589.pdf
28. Itonaga, T., Tokuuye, K., & Mikami, R. (2022). Mathematical evaluation of salivary gland function post-radiotherapy. The British Journal of Radiology. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8822577/
29. Zhang, Q., Ouyang, H., & Ye, F. (2020). Multiple mathematical models for endometrial cancer characterization. European Journal of Radiology. Retrieved from https://www.sciencedirect.com/science/article/pii/S0720048X20302916
30. Liu, J., Cai, Y., & Huang, D. (2022). Saliva diagnostics: Emerging techniques and biomarkers for cancer detection. Expert Review of Molecular Diagnostics. Retrieved from https://drive.google.com/file/d/1Y2jbE3MZwKtbfDwEvXGecfSlNjOCvij5/view
31. 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
32. 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
33. Nishanov Akhram Khasanovich, Mamazhanov Rakhmatilla Yakubzhanovich, Khaidarov Sherali Islom o‘g‘li, Xolbekov Abdusattor Maxammatovich, & Karimova Zilola Botirovna. (2024). Diagnostic Algorithm for Early Detection of Breast Cancer Based on Error Minimization Approach. International Journal of Innovative Science and Research Technology (IJISRT), 9(12), 1535–1542. https://doi.org/10.5281/zenodo.14565219
Загрузки
Дополнительные файлы
Опубликован
Как цитировать
Лицензия
Copyright (c) 2025 Sherali Xaydarov, Akhram Nishanov, Raxmatilla Mamajanov, Farxod Mengturayev, Rustam Yuldashev
Это произведение доступно по лицензии Creative Commons «Attribution» («Атрибуция») 4.0 Всемирная.
