Table 15 A comparison of the accuracy of different learning algorithms applied over lung cancer.
From: Performance of machine learning algorithms for lung cancer prediction: a comparative approach
S. No. | Model name | Accuracy (%) |
|---|---|---|
1 | Logistic Regression | 87.5 |
2 | Gaussian Naive Bayes | 91.07 |
3 | Bernoulli Naive Bayes | 91.07 |
4 | Support Vector Machine | 85.71 |
5 | Random Forest | 85.71 |
6 | K-Nearest Neighbors | 92.86 |
7 | Extreme Gradient Boosting | 89.29 |
8 | Extra Tree | 89.29 |
9 | ADA Boost | 89.29 |
10 | Ensemble_1 with XGB and ADA | 89.29 |
11 | Ensemble_2 with Voting Classifier | 87.5 |
12 | MLP | 89.29 |
