Table 6.

The comparative study between the proposed ML regression model and [37], [39] using the same database.

Author	Methodology	Dataset used	Problem Type	Metrics	Comment
Ordás et al. [37]	(PCA, K-means Algorithm)	https://www.kaggle.com/mariaren/covid19-healthy-diet-dataset	Classification	Accuracy = 95%	The correlations between the eating habits and death cases of 170 countries during the COVID-19 pandemic were assessed to find the relationship between these habits and death rates-based ML.
Shams et al. [39]	SVM Model based on RBF	https://www.kaggle.com/mariaren/covid19-healthy-diet-dataset	Classification	Accuracy = 99.73%	This architecture can forecast the human cases affected by the COVID-19 pandemic due to each patient's diet habits and system.
	SVM Model with Linear			Accuracy = 99.83%
	SVM Model with Linear Kernel			Accuracy = 79.30%
	Deep Learning			Accuracy = 99.72%
Our Proposed (HANA Model)	Elastic Net Regression	https://www.kaggle.com/mariaren/covid19-healthy-diet-dataset https://www.kaggle.com/mariaren/covid19-healthy-diet-dataset	Regression	MSE = 0.00018113	This proposed regression model able to forecast the human cases affected by the COVID-19 pandemic due to each patient's diet habits and system using MSE.
	(PCA, Backpropogation Neural Netwroks)		Classification	Accuracy = 98.76%