# HD3C: Efficient Medical Data Classification for Edge Devices

arXiv: https://arxiv.org/abs/2509.14617 · HTML: https://mlsanity.com/paper/2509.14617/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 25 references that are in the map.
- First posted on arXiv 18 Sep 2025.

## Builds on (most cited first, 5 of 5)

- [An EMG Gesture Recognition System with Flexible High-Density Sensors and Brain-Inspired High-Dimensional Classifier](https://mlsanity.com/paper/1802.10237/) (2018)
- [HyperEmbed: Tradeoffs Between Resources and Performance in NLP Tasks with Hyperdimensional Computing enabled Embedding of n-gram Statistics](https://mlsanity.com/paper/2003.01821/) (2020)
- [Dual Bayesian ResNet: A Deep Learning Approach to Heart Murmur Detection](https://mlsanity.com/paper/2305.16691/) (2023)
- [ENACT-Heart -- ENsemble-based Assessment Using CNN and Transformer on Heart Sounds](https://mlsanity.com/paper/2502.16914/) (2025)
- [NRC-Net: Automated noise robust cardio net for detecting valvular cardiac diseases using optimum transformation method with heart sound signals](https://mlsanity.com/paper/2305.00141/) (2023)

## Abstract (arXiv)

Efficient medical data classification is essential for modern disease screening, particularly in resource-constrained environments where power budgets and computing capabilities are limited. We present HD3C, a lightweight classification framework designed for low-power edge devices. HD3C encodes data into high-dimensional hypervectors, aggregates them into multiple cluster prototypes, and performs classification through similarity search in hyperspace. We evaluate HD3C across three medical classification tasks; on heart sound classification, HD3C is 350x more energy-efficient than Bayesian ResNet with less than 1% difference in accuracy. Moreover, HD3C demonstrates exceptional robustness to noise, limited training data, and hardware error, supported by both theoretical analysis and empirical results, highlighting its potential for reliable deployment in real-world settings.