A Fusion-Based Approach for Classifying Imbalanced Medical Datasets  Using Meta-Learning and Zero-Shot Inference

Darapureddy Padmanjani ,Kopparthi Suresh

A Fusion-Based Approach for Classifying Imbalanced Medical Datasets Using Meta-Learning and Zero-Shot Inference

Authors

Darapureddy Padmanjani ,Kopparthi Suresh

Keywords:

Meta-Learning, Zero-Shot Learning, Imbalanced Medical Data, Heart Disease Prediction, Hepatitis Classification, Healthcare AI, Machine Learning, Prototypical Networks

Abstract

Imbalanced medical datasets present major challenges in healthcare analytics, particularlyin disease prediction systems where minority classes are often underrepresented. This research proposes a fusion-based classification framework

References

ware*, S., Rakesh, S. K., & Choudhary, B. (2020). Heart Attack Prediction by using Machine Learning Techniques. International Journal of Recent Technology and Engineering (IJRTE), 8(5), 1577–1580. https://doi.org/10.35940/ijrte.D9439.018520

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Published

2026-05-27

How to Cite

Darapureddy Padmanjani ,Kopparthi Suresh. (2026). A Fusion-Based Approach for Classifying Imbalanced Medical Datasets Using Meta-Learning and Zero-Shot Inference. Journal of Computational Analysis and Applications (JoCAAA), 35(5), 333–346. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/5500