Table 1.
Summary of related work.
| Sr. No. | Author | Year | Machine Learning Algorithms and Accuracy (%) |
|---|---|---|---|
| 1. | A. J. Aljaaf et al. [1] | 2018 | Naïve Bayes: 83.4%, J48: 86.23% |
| 2. | N. Borisagar, D. Barad, and P. Raval [5] | 2017 | ANN: 99.5 |
| 3. | B. Boukenze, A. Haqiq, and H. Mousannif [6] | 2018 | SVM: 63.5%, LR: 64.0, C4.5: 63%, KNN: 55.15% |
| 4. | H. Polat, H. D. Mehr and A. Cetin [7] | 2019 | SVM: 97.5% |
| 5. | P. Panwong and N. Iam-On [8] | 2016 | KNN: 86.32%, naïve Bayes: 60.46%, ANN: 83.24%, RF: 86.60%, J48: 79.52% |
| 6. | Makino et al. [11] | 2019 | KNN, Naïve Bayes + LDA + random subspace + Tree-based decision: 94% |
| 7. | Ren et al. [12] | 2019 | SVM + ReliefF: 92.7% |
| 8. | Ma F. et al. [13] | 2019 | Fisher discriminatory analysis and SVM: 96.7% |
| 9. | Almansour and colleagues [14] | 2020 | KNN and SVM: 99% |
| 10. | J. Qin and colleagues [15] | 2019 | SVM, KNN, and naïve Bayes decision tree: 99.7% |
| 11. | Z. Segal and colleagues [16] | 2019 | SVM, KNN, and decision tree: 99.1% |
| 12. | Khamparia et al. [17] | 2020 | Logistic regression, KNN, SVM, random forest, naive Bayes, and ANN: 99.7% |
| 13. | Ebiaredoh-Mienye Sarah A. et al. [18] | 2017 | SVM 98.5% |
| 14. | Zhiyong Pang et al. [19] | 2020 | Softmax regression 98% |
| 15. | Tabassum, Mamatha et al. [23] | 2017 | DT: 85%, RF: 85% |
| 16. | K. R. A. Padmanaban and G. Parthiban [24] | 2016 | DT: 91%, naïve Bayes: 86% |
| 17. | Sahil Sharma, Vinod Sharma, and Atul Sharma [25] | 2018 | ANN: 80.4%, RF: 78.6% |
| 18. | Pratibha Devishri [26] | 2019 | ANN: 86.40%, SVM: 77.12% |
| 19. | Sujata Drall, G. Singh Drall, S. Singh, Bharat Naib [27] | 2018 | Naïve Bayes: 94.8%, KNN: 93.75%, SVM: 96.55% |
LR: Logistic Regression; KNN: k-Nearest Neighbors; SVM: Support Vector Machines; CART: Classification and Regression Trees; ANN: Artificial Neural Networks; LDA: Linear Discriminant Analysis; DT: Decision Tree; RF: Random Forest.