<![CDATA[Lung diseases, including tuberculosis (TB) and lung cancer, are major health issues in Indonesia with high incidence and mortality rates. Early and accurate diagnosis is crucial to increase the chances of patient recovery. This research aims to develop a predictive model for lung diseases using the Deep Neural Network (DNN) algorithm. The dataset used in this study consists of 30,000 health records obtained from the Kaggle website, with 52% of the data indicating the presence of lung diseases. The research process includes data collection, data pre-processing, DNN model development, and model evaluation using a confusion matrix. The results show that the developed predictive model achieved an accuracy of 94%, with high precision and recall values for both positive and negative classes. The model evaluation indicates that it is capable of identifying lung disease cases with a low error rate]]>
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