Machine Learning Models for Analyzing Ethnic Influence on Bone Component Changes in Women's Aging Process

Abstract

The study involves a privacy-sensitive federated multimodal learning model aimed at evaluating the ethnic role in the alterations of bone components during the aging process of women. The proposed model includes imaging, clinical and demographic data with the help of which prediction accuracy will be increased and data will remain confidential, implemented with the help of TensorFlow Federated (TFF). The framework uses an ethnicity-conscious loss objective in minimizing inter-ethnic bias and endorsing fairness among various populations. Federated learning enables several medical institutions to jointly train models without accessing sensitive information about patients, thus providing good privacy. The experimental analysis shows that the given approach yields better performance, with the accuracy of 94.2 percent, and the ethnic performance difference is much smaller than in the case of traditional frameworks. The explainability analysis indicates ethnicity-specific patterns of bone variation, which justifies individual osteoporosis risks evaluation. All in all, the study provides a safe, explainable, and fair machine learning model to further develop the analysis of bone health of women in all nationalities on Earth.

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