<![CDATA[Breast cancer is one of the leading causes of cancer-related mortality among women, highlighting the need for faster and more accurate approaches to support disease staging. The utilisation of electronic medical records through data mining techniques provides an alternative approach for breast cancer stage classification. This study aimed to analyse breast cancer stage classification using the Naïve Bayes algorithm based on electronic medical record data from patients at Baladhika Husada Level III Hospital, Jember. A quantitative approach was employed using secondary data consisting of 476 breast cancer medical records selected from a total of 1,082 records. The research stages included data selection, data cleaning, categorical encoding, model development using the Naïve Bayes algorithm, and model evaluation using a Confusion Matrix based on accuracy, precision, and recall. Model performance was evaluated using nine training-testing split scenarios ranging from 10:90 to 90:10. The experimental results showed that the 90:10 split scenario achieved the best performance, with an accuracy of 87.50%, precision of 86.36%, and recall of 86.36%. These findings indicate that the Naïve Bayes algorithm is capable of classifying breast cancer stages effectively based on patients' clinical characteristics recorded in electronic medical records. The proposed approach demonstrates the potential of integrating electronic medical records and the Naïve Bayes algorithm to support the development of clinical decision support systems for breast cancer stage classification.]]>
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