Article

COMPARATIVE ANALYSIS OF MACHINE LEARNING MODELS IN EARLY DIAGNOSIS OF OSTEOPOROSIS

Shahzoda AnarovaProfessor Tashkent University of Information Technologies named after Muhammad al-KhwarizmiSarvar OmonovMaster's Student Tashkent University of Information Technologies named after Muhammad al-Khwarizmi

Zenodo (CERN European Organization for Nuclear Research)repository2026

ABI

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.

Topics

Artificial Intelligence in Healthcare Healthcare Systems and Public Health Artificial Intelligence in Healthcare and Education

Identifiers

DOI: 10.5281/zenodo.19644869

Citations and references

Cited by 00 references

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