COMPARATIVE ANALYSIS OF MACHINE LEARNING MODELS IN EARLY DIAGNOSIS OF OSTEOPOROSIS

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.

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