Vol. 1 No. 1 (2026), Articles
Vol. 1 No. 1 (2026)

COMPARATIVE ANALYSIS OF MACHINE LEARNING MODELS IN EARLY DIAGNOSIS OF OSTEOPOROSIS

Articles
Published 2026-04-18
Shahzoda Anarova
Professor Tashkent University of Information Technologies named after Muhammad al-Khwarizmi
Sarvar Omonov
Master’s Student Tashkent University of Information Technologies named after Muhammad al-Khwarizmi
PDF

Keywords

osteoporosis, machine learning, CNN, XGBoost, Random Forest, SVM, bone mineral density, DEXA.

How to Cite

Anarova , S., & Omonov , S. (2026). COMPARATIVE ANALYSIS OF MACHINE LEARNING MODELS IN EARLY DIAGNOSIS OF OSTEOPOROSIS. Kelajak Texnologiyalari Va sun’iy Intellekt, 1(1), 41-51. https://doi.org/10.5281/zenodo.19644870
ACM ACS APA ABNT Chicago Harvard IEEE MLA Turabian Vancouver AMA

Download Citation

Endnote/Zotero/Mendeley (RIS) BibTeX

Abstract

This article presents a comparative analysis of machine learning models used for the early diagnosis of osteoporosis, including Random Forest, XGBoost, Convolutional Neural Networks (CNN), Support Vector Machine (SVM), and logistic regression. The accuracy metrics, advantages, and limitations of these models are discussed. The diagnostic effectiveness of models built on medical imaging, bone mineral density (BMD) indicators, and clinical data is demonstrated. The research findings have practical significance in automating decision-making processes for physicians.

PDF

References

1. Anarova Sh.A., Omonov S.A. Suyak yemirilishi kasalliklarining hozirgi kundagi dolzarbligi // Yangi Oʻzbekistonda tabiiy va ijtimoiy-gumanitar fanlar. - 2025. - Vol. 3, Issue 4. - B. 90–94.

2. Anarova Sh.A., Omonov S.A. Suyak yemirilishi kasalliklarini erta tashxislashda katta tibbiy maʼlumotlar va Data Mining usullarining qoʻllanilishi // Suniy intellekt nazariyasi va amaliyoti: tajriba, muammolar va istiqbollar: II Respublika ilmiy-amaliy anjumani materiallari. - 17–18 iyun, 2025.

3. Stromsnes K., Fajardo C.M., et al. Osteoporosis: Causes, Mechanisms, Treatment and Prevention: Role of Dietary Compounds // Pharmaceuticals. - 2024. - Vol. 17. - P. 1697.

4. Özmen S., Kurt S., Timur H.T., et al. Prevalence and Risk Factors of Osteoporosis: A Cross-Sectional Study in a Tertiary Center // Medicina. - 2024. - Vol. 60. - P. 2109.

5. Wang J., Yan D., Wang S., et al. Osteopenia Metabolomic Biomarkers for Early Warning of Osteoporosis // Metabolites. - 2025. - Vol. 15. - P. 66.

6. Chen T., Guestrin C. XGBoost: A Scalable Tree Boosting System // Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. - 2016. - P. 785–794.

7. Ristevski B., Chen M. Big Data Analytics in Medicine and Healthcare // Journal of Integrative Bioinformatics. - 2018. - Vol. 15, No. 3. - P. 1–14.

8. Esteva A., et al. Dermatologist-level classification of skin cancer with deep neural networks // Nature. - 2017. - Vol. 542, No. 7639. - P. 115–118.

9. Compston J.E., McClung M.R., Leslie W.D. Osteoporosis // The Lancet. - 2019. - Vol. 393, No. 10169. - P. 364–376.

10. Toshmamatov B.N., Gʻulomov Sh.S., Botirov K.N. Suyak yemirilishi kasalligi // Amaliy va tibbiyot fanlari ilmiy jurnali. - 2024. - B. 172–175.

11. Tański W., Kosiorowska J., Szymańska Ch.A. Osteoporosis - risk factors, pharmaceutical and non-pharmaceutical treatment // European Review for Medical and Pharmacological Sciences. - 2021. - P. 3557–3566.

12. Salari N., et al. The global prevalence of osteoporosis in the world: A comprehensive systematic review and meta-analysis // Journal of Orthopaedic Surgery and Research. - 2021. - Vol. 16. - P. 609.