Model Ensemble Algoritma Naive Bayes Dan Random Forest Dalam Klasifikasi Penyakit Paru-paru Untuk Meningkatkan Akurasi
DOI:
https://doi.org/10.37476/smartlock.v2i2.4407Keywords:
Naïve Bayes, Random Forest, Accuracy, ClassificationAbstract
This study explores an ensemble model combining Naive Bayes and Random Forest algorithms in the classification of lung diseases with the aim of improving accuracy. Involving a dataset of 30,000 instances, the research yields excellent performance in various aspects, including accuracy, precision, and recall. The combination of the Naïve Bayes model with Random Forest integrated using the VotingClassifier proves superior compared to using the Naïve Bayes model alone. Experimental results demonstrate that the ensemble model achieves an accuracy of 93%, with precision reaching 87.03%, and a recall of 100%. This superiority emphasizes that integrating the strengths of Naive Bayes and Random Forest in an ensemble approach can enhance the predictive capabilities of the classification system. The study significantly contributes to improving the diagnosis of lung diseases, opening opportunities for the development of more efficient classification systems in medical practice. Thus, this research not only enhances classification accuracy but also provides guidance for the development of artificial intelligence-based solutions in the healthcare domain.
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