Multi-model fusion bone mineral density value prediction method and system

Through the multi-model fusion method, multiple machine learning models are used to predict bone density, which solves the problem that a single model is difficult to adapt to complex influencing factors, and achieves higher prediction accuracy and robustness.

CN119724553AActive Publication Date: 2025-03-28SHANDONG NORMAL UNIV
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Patent Information

Application Number
CN202411841270.5
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-13
Publication Date
2025-03-28
Estimated Expiration
2044-12-13

AI Technical Summary

Technical Problem

Existing machine learning-based bone density prediction methods rely on a single model and are difficult to adapt to complex factors affecting bone density, resulting in unsatisfactory prediction accuracy. Especially in the face of incomplete noise data or characteristics, the performance of a single model is prone to significantly decline.

Method used

Using the multi-model fusion method, multiple machine learning models are designed as multi-level prediction architectures. Each level of model learns and optimizes the prediction results and original input features of the previous stage, improves the prediction accuracy through adaptive calculation of the fusion weight, and further optimizes the fusion results using a meta-learner.

Benefits of technology

Through the multi-model fusion method, the robustness of the prediction is enhanced, the impact of low-precision models on the final prediction results is reduced, the deviation in the prediction of the basic learner is eliminated, the performance of the model on unknown data is ensured, and the accuracy of the bone density prediction value is finally provided.

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Abstract

The invention relates to the technical field of bone mineral density value prediction, and provides a multi-model fusion bone mineral density value prediction method and system, and the method comprises the following steps: obtaining clinical feature data of bone mineral density prediction; constructing a multi-model fusion prediction model comprising base learners and meta learners which are connected in sequence, and inputting the obtained features into the plurality of base learners for prediction identification to obtain a primary prediction result; according to the prediction accuracy of the base learners, adaptively calculating a fusion weight, and fusing the primary prediction results of the base learners based on the fusion weight obtained by calculation; and transmitting the fused primary prediction result to a meta-learner for prediction to obtain a prediction result of the bone mineral density. According to the invention, a plurality of machine learning models are designed as a multi-stage prediction architecture, the model of each stage learns and optimizes the prediction result of the previous stage and the original input features, and the prediction accuracy is improved stage by stage.
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