Comparative Analysis of Naïve Bayes and SVM Algorithms for Early Detection of Lung Disease at Cimuning Community Health Center

This study develops an early prediction model for lung disease using the Naïve Bayes algorithm and Support Vector Machine (SVM) at the Cimuning Health Center to overcome the limitations of diagnostic tools and experts. The evaluation results show that Naïve Bayes is superior with accuracy, precision, recall, and F1-score of 1.00. The research methods include business understanding, data preparation, modeling, evaluation, and implementation, with patient data that has been cleaned and analyzed. The confusion matrix is ​​used to assess model performance. The results show that Naïve Bayes is effective in early prediction of lung disease, so this system is expected to help health centers in detecting patients who need to be referred to the hospital.