PREDIKSI PASIEN KANKER PARU-PARU MENGGUNAKAN METODE K-NEAREST NEIGHBORS, DECISION TREE, DAN NAIVE BAYES
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Abstract
This research aims to determine the application performance of the K-Nearest Neighbors (KNN), Decision Tree, and Naive Bayes methods in predicting lung cancer patients. Lung cancer is a deadly disease that is often difficult to detect in its early stages. Therefore, the development of accurate and efficient prediction models has a significant impact in early diagnosis and improving patient survival rates. This research yields a deeper understanding of the performance of these methods in the context of lung cancer prediction. The dataset used includes information such as age, gender, and other medical history of the lung cancer patients observed. Experimental results show that Decision Tree has the highest accuracy, followed by KNN and Naive Bayes. However, these three methods provide valuable contributions in the context of lung cancer prediction. These findings can be a basis for further development in the field of cancer diagnosis and provide valuable insights for medical practitioners and researchers in efforts to improve the effectiveness of early lung cancer detection.
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