Table 5 The comparison between the models generated in this study and the models developed in previous studies.
Researcher | ML method | Hyperparameter tuning | Number of data | Accuracy (%) | Year |
|---|---|---|---|---|---|
Zhou et al.44 | Stochastic gradient boosting | tenfold cross validation | 254 | 61.22 | 2016 |
Liang et al.6 | Random forest, adaptive boosting, gradient boosted decision tree, extreme gradient boosting, and light gradient boosting machine | fivefold cross validation | 93 | 80.0 | 2020 |
Li et al.47 | Artificial neural network | - | 254 | 71.0 | 2020 |
Ji et al.46 | Support vector machine | Genetic optimization | 132 | 88.0 | 2020 |
Liang et al.66 | Logistic regression, naive Bayes, Gaussian process, multilayer perceptron neural network, support vector machines, and decision tree | fivefold cross validation | 91 | 86.6 | 2021 |
Yin et al.67 | Convolutional neural network | Adaptive moment optimization and Bayes optimization | 400 | 91.67 | 2021 |
Maxutov and Adoko68 | Bayesian network | - | 254 | 78.0 | 2021 |
Kamran et al.69 | K-Nearest neighbor | - | 93 | 96.0 | 2022 |
Ullah et al.4 | Extreme gradient boosting | Grid search | 93 | 88.0 | 2022 |
Jin et al.13 | Categorical gradient boosting | Grid search | 99 | 89.5 | 2023 |
Qiu and Zhou70 | Light gradient boosting machine, extreme gradient boosting, random forest, support vector machine, and logistic regression | LévyFlight-Jaya optimization | 91 | 89.3 | 2023 |
Qiu and Zhou48 | Extreme gradient boosting | Sand cat swarm optimization | 254 | 88.4 | 2023 |
Sun et al.71 | Random forest | - | 105 | 85.7 | 2023 |
Sun et al.72 | Weighted probability stacking | Bayesian optimization | 114 | 91.3 | 2024 |
This study | Random forest | Coati optimization | 93 | 83.3 | 2025 |
This study | Random forest | Whale optimization | 93 | 94.4 | 2025 |
