<![CDATA[Heart disease is one of the leading causes of global death, often difficult to detect early due to non-specific clinical symptoms. To overcome the limitations of manual diagnosis, the application of data mining techniques utilizing the Naïve Bayes algorithm presents an efficient and accurate computational solution. This study aims to analyze and map the effectiveness of Naïve Bayes implementation in predicting heart disease through a Systematic Literature Review (SLR) approach. The contribution of this study is to provide a comprehensive taxonomic guide regarding the influence of data geometry, preprocessing techniques, and the integration of feature selection methods on optimizing the performance of probabilistic models. The results of the literature review indicate that the model accuracy level varies between 58% and 91.80%, with the majority of performance stable in the range of 79%-91% which is deterministically influenced by the quality of data dimensionality reduction. Overall, the Naïve Bayes-based data mining process has proven to have great potential as a clinical decision support system in supporting early medical preventive measures.]]>
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