Dimensionality-Performance Trade-Off Analysis of the Hybrid Character-Word Embedding (HCWE) Architecture for Medical Text Classification Under Noise Conditions

Medical text classification is critical for applications such as automated triage and disease surveillance. However, real-world data is often corrupted by character-level noise, which substantially degrades the performance of traditional models. Although the Hybrid Character-Word Embedding (HCWE) architecture has demonstrated promising results, the optimal embedding dimensionality for balancing classification performance and computational efficiency under noisy conditions remains unclear. This study presents a comprehensive analysis of the relationship between embedding dimensionality and classification performance by evaluating the HCWE architectural family across embedding dimensions ranging from fifty to five hundred under noise levels ranging from zero percent to one hundred percent. A medical tweet dataset containing 2497 samples across 15 health categories was evaluated using a 5-fold stratified cross-validation protocol to ensure statistical robustness. The results show that HCWE-200D achieves a peak Macro F1-score of 0.860 ± 0.012 at a severe 60% noise level, exceeding the strongest baseline by 34.4% in relative performance improvement (p < 0.01, paired t-test). Direct feature-space analysis reveals that increasing dimensionality from 50-D to 500-D reduces the hash collision rate from 99.19% down to 91.90% while increasing feature vector sparsity from 2.67% to 66.33%, explaining why performance reaches a clear saturation plateau beyond 300 dimensions. Under realistic structured noise (keyboard typos, phonetic misspellings, character repetitions, and medical slang), HCWE maintains robust performance (Macro F1 = 0.248 to 0.273). Furthermore, a multi-objective deployment utility function U(D) demonstrates that HCWE-50D optimizes resource-constrained edge deployments (RAM < 15 MB, latency 0.12 ms), whereas HCWE-200D and HCWE-300D represent the optimal configurations for balanced and high-accuracy server-side deployments, respectively.

@article{Suhaidi2026,
  title     = {Dimensionality-Performance Trade-Off Analysis of the Hybrid Character-Word Embedding (HCWE) Architecture for Medical Text Classification Under Noise Conditions},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {8},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Mustazzihim Suhaidi and Rabiah Binti Abdul Kadir and Sabrina Tiun},
  doi       = {10.14569/IJACSA.2026.01708102},
  url       = {https://doi.org/10.14569/IJACSA.2026.01708102}
}