Application of Model Combination Methods in Medical Diagnostics

DOI

10.2991/978-94-6239-774-3_2How to use a DOI?

Keywords

Model combination methods; Medical diagnosis; Accuracy

Abstract

With the explosive growth of medical data and the rapid development of AI technology, medical diagnosis is transforming from traditional experience-driven to data-driven, and a single machine learning model is often difficult to meet the clinical requirements in medical diagnosis due to problems such as incomplete feature capture and insufficient generalization capability. Model combination methods have become a hot research topic in the field of medical diagnosis by integrating the advantages of multiple models, effectively balancing the bias and variance, and significantly improving the accuracy and stability of medical diagnosis. This paper systematically expounds the theoretical foundations of model combination methods, comprehensively detailing the principles and application scenarios of common approaches including simple averaging, weighted averaging, bagging, boosting, and stacking, thoroughly investigates the core advantages of model combination methods in enhancing medical diagnostic performance, analyzes current challenges such as high computational costs and poor interpretability, and prospects future integration with deep learning and multimodal fusion technologies. The model combination method provides scientific and reliable technical support for medical diagnosis and has important clinical application value and academic research significance.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Yuanan Lu
PY  - 2026
DA  - 2026/09/11
TI  - Application of Model Combination Methods in Medical Diagnostics
BT  - Proceedings of the 2026 5th International Conference on Mathematical Statistics and Economic Analysis (MSEA 2026
PB  - Atlantis Press
SP  - 4
EP  - 14
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6239-774-3_2
DO  - 10.2991/978-94-6239-774-3_2
ID  - Lu2026
ER  -