Enhancing Multi-Class Disease Classification: Neoplasms, Cardiovascular, Nervous System, and Digestive Disorders Using Advanced LLMs

• URL: http://arxiv.org/abs/2411.12712v1
• Date: Tue, 19 Nov 2024 18:27:25 GMT
• Title: Enhancing Multi-Class Disease Classification: Neoplasms, Cardiovascular, Nervous System, and Digestive Disorders Using Advanced LLMs
• Authors: Ahmed Akib Jawad Karim, Muhammad Zawad Mahmud, Samiha Islam, Aznur Azam,
• Abstract summary: BioBERT, which was pre-trained on medical data, demonstrated superior performance in medical text classification. XLNet, a custom model based on the lighter version of BERT, also proved competitive with 87.10% accuracy.
• Score: 0.0
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• Abstract: In this research, we explored the improvement in terms of multi-class disease classification via pre-trained language models over Medical-Abstracts-TC-Corpus that spans five medical conditions. We excluded non-cancer conditions and examined four specific diseases. We assessed four LLMs, BioBERT, XLNet, and BERT, as well as a novel base model (Last-BERT). BioBERT, which was pre-trained on medical data, demonstrated superior performance in medical text classification (97% accuracy). Surprisingly, XLNet followed closely (96% accuracy), demonstrating its generalizability across domains even though it was not pre-trained on medical data. LastBERT, a custom model based on the lighter version of BERT, also proved competitive with 87.10% accuracy (just under BERT's 89.33%). Our findings confirm the importance of specialized models such as BioBERT and also support impressions around more general solutions like XLNet and well-tuned transformer architectures with fewer parameters (in this case, LastBERT) in medical domain tasks.

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