Comparison of Naive Bayes and Decission Tree Algorithms for Lung Cancer Disease Prediction

Abstract

In this study, we compared the performance of two machine learning algorithms, Naïve Bayes and Decission Tree, for diagnosing lung diseases using patient health datasets. The main objective of this study is to evaluate the accuracy, precision, recall, and F1 score of the two algorithms to determine which method is more effective in predicting lung diseases. The results showed that the tree classification algorithm outperformed Naïve Bayes in terms of accuracy, reaching 95% in an 80:20 split, compared to the 78% accuracy achieved by Naïve Bayes on the same data. Further analysis showed that most patients in this dataset were high risk with 365 patients, followed by risk with 332 patients, and low risk with 303 patients. The decision tree structure proved to be more effective in handling the complexity of the data and produced more accurate predictions, improving efficiency by creating a new "Risk_Score". These results show that decision trees are a better method than Naïve Bayes for diagnosing lung diseases and can provide a solid foundation for developing accurate machine learning models for future health research.