<![CDATA[The rising incidence of lung cancer is often outpaced by the constraints of medical resources, creating a critical gap that frequently leads to diagnostic delays and hindered treatment interventions. Early-stage detection is therefore fundamentally important to boost therapy efficacy and extend patient survival rates. This study proposes a computational diagnostic model utilizing the Naïve Bayes algorithm to classify lung cancer status based on structured clinical datasets encompassing demographic profiles, smoking behaviors, and patient reported symptoms. Data integrity was prioritized through rigorous preprocessing, which included handling missing values and transforming categorical variables into a numerical format suitable for probabilistic modeling. The Naïve Bayes framework was chosen for its interpretability, robustness, and efficiency in handling tabular clinical data. Empirical evaluations yielded highly promising results, with the proposed model achieving an accuracy of 95%, precision of 98%, recall of 96%, and an F1-score of 97%. These findings validate the capability of the algorithm to provide dependable diagnostic predictions, demonstrating its potential as a decision support tool to streamline early screening processes for lung cancer. ]]>
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