Naïve Bayes Alpha Parameter Optimization with Ant Colony for Clinical Text Classification
DOI:
https://doi.org/10.31849/digitalzone.v16i1.24118Keywords:
Classification, NLP, Naive Bayes, ACO, classification, NLP, Naïve Bayes, ACO, medical textAbstract
This study addresses the challenges of text classification in domain-specific Natural Language Processing (NLP) within the medical field, which differs significantly from general NLP due to the presence of complex medical jargon and informal language in clinical documents. The primary objective of this research is to develop and evaluate a cancer-related text classification model by integrating the Naïve Bayes algorithm with Laplacian smoothing and optimizing its alpha parameter using Ant Colony Optimization (ACO). Specifically, the study aims to determine whether ACO can effectively identify the optimal alpha value that enhances the classification performance of the Naïve Bayes model. Experimental results demonstrate that with an alpha value of 0.27, the proposed model achieves an accuracy of 81.05%. This indicates that the combination of ACO and Naïve Bayes significantly improves classification efficiency and accuracy. The findings contribute to more accurate interpretation of clinical cancer-related texts, supporting better-informed decision-making in medical contexts
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