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This paper presents an interpretable machine learning approach using a naive Bayes classifier on a small clinical dataset to predict cognitive impairment from inflammatory biomarkers, identifying I-309 (CCL1) as a key predictive feature.
The paper benchmarks Naive Bayes against large language models for text classification, finding that NB remains competitive with LLMs when labeled data is available, offering higher throughput and lower energy consumption for resource-constrained environments.