<![CDATA[The global increase in lung disease cases presents a serious healthcare challenge requiring early detection systems for optimal treatment. This study examines the implementation of the Decision Tree algorithm in classifying various types of lung diseases based on a comprehensive analysis of recent studies. The methodology employs a Systematic Literature Review (SLR) approach by thoroughly analyzing five selected scientific publications published between 2023-2024. Evaluation results demonstrate that the Decision Tree algorithm shows promising performance in lung condition classification with accuracy ranges from 56.7% to 99.67%. Research findings indicate that Decision Tree algorithm optimization can be achieved through the integration of appropriate data preprocessing techniques and careful feature selection. Based on the analysis conducted, it can be concluded that Decision Tree is a reliable method for lung disease classification, particularly when implemented with optimized parameter configurations and proportional datasets.]]>
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