Class-Dependent Hybrid Data Augmentation for Multiclass Migraine Classification under Severe Class Imbalance
arXiv:2605.23453v1 Announce Type: new Abstract: We conducted a reproducibility-oriented re-evaluation of prior migraine classification studies, correcting for data leakage and metric bias. We then introduced (i) a clinically motivated aggregation of two hemiplegic subtypes following ICHD-3 {\S}1.2.3, (ii) a class-dependent hybrid augmentation strategy that assigns generation methods based on per-class sample size, and (iii) the concept of fidelity asymmetry, motivating proportionally constrained growth as an alternative to full class balance. Experiments were performed on a dataset of 400 pati
The proliferation of AI in medical diagnostics necessitates robust, reproducible methods, while limited, imbalanced datasets are a common challenge in healthcare AI development.
Improving AI classification accuracy and addressing data imbalance in medical applications can lead to more reliable diagnostic tools and better patient outcomes, especially for complex conditions like migraines.
This research introduces methodological advancements for data augmentation and re-evaluation in medical AI, potentially setting new standards for reproducibility and clinical applicability in specialized fields.
- · AI-driven medical diagnostics companies
- · Patients with complex conditions
- · Medical researchers using deep learning
- · Healthcare providers
- · AI models suffering from data leakage
- · Over-reliant, unvalidated diagnostic tools
- · Data scientists ignoring class imbalance
More accurate and reliable AI models for migraine classification will emerge, improving diagnostic consistency.
The methodology could be generalized to other medical conditions with severe class imbalance, accelerating AI adoption in diagnostics.
Increased trust in AI healthcare applications could lead to greater investment and regulatory approval for AI-powered diagnostic platforms.
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