<![CDATA[One of the leading causes of death worldwide is lung cancer, with smoking being the biggest risk factor contributing to nearly 80% of cases. Exposure to carcinogens such as radon, asbestos, and air pollution also increases the risk of developing this disease. Using a dataset that includes various medical factors, this study attempts to apply the Naïve Bayes algorithm to lung cancer classification. Naïve Bayes was chosen because of its high accuracy in classifying difficult data. The results of the study show that Naïve Bayes has an accuracy rate of 92.93% in identifying lung cancer, which is influenced by the quantity of related features and data quality. This algorithm can help detect and prevent lung cancer early, thereby supporting the development of more effective strategies and early diagnosis. The results of this study are expected to be used as a reference in the development of data mining-based medical decision support systems, particularly for the early detection of lung cancer.]]>
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