Table 1.

Different Machine learning, deep learning algorithms with different architectures.

Study	Method	Accuracy
[22]	fusion Model ( LR and RF)	99.83%
[23]	J48 Classifier	99.00%
[24]	RF algorithm with RFE feature selection	89%
[25]	LSVM with full features	98.86%
[26]	RF with Random Forest Feature Selection	98.8%
[27]	MLP Classifier with genetic search algorithm	98.1%
[28]	Random Subspace method with KNN classifier	97.2%
[29]	Gradient Boosting Machines (GBM)	97.5%
[30]	Deep learning with Convolutional neural Networks (CNN)	98.3%
[31]	XGBoost with feature selection	99.2%
[32]	Ensemble Learning using stacking (LR, KNN and SVM)	98%
[33]	LightGBM with Bayesian Optimization	99.0%
[34]	CatBoost With feature selection	98.2%
[35]	Extreme Learning Machines (ELM)	97.3%