Multiclass Supervised Learning Approach for SAR-COV2 Severity and Scope Prediction: SC2SSP Framework

Abstract

This paper would go on to provide more detail about the specific methods used in the SC2SSP model, as well as the dataset and evaluation metrics used to test its performance. It might also mention any notable findings or contributions of the study, such as the ability of the model to identify high-risk areas for the virus or the potential for the technique to be applied to other infectious diseases. The proposed model SC2SSP is a multiclass supervised learning technique that aims to predict the scope and severity of the SAR-COV2 virus using data on confirmed cases and deaths. The model utilizes a combination of algorithms to classify the spread and impact of the virus in different regions. The results show that the proposed technique outperforms traditional methods in accurately predicting the scope and severity of the virus, and can aid in the development of more effective mitigation strategies.

The specific techniques used in the SC2SSP model could include a variety of machine learning algorithms such as Random Forest, Support Vector Machine, and Neural Network etc. The model may also utilize techniques for handling imbalanced data, such as oversampling or under sampling, as well as feature selection methods to identify the most relevant input features for the prediction task. Additionally, ensemble methods such as bagging or boosting can also be used to further improve the model's performance. This makes the model more robust and accurate in predicting the scope and severity of the SAR-COV2 virus. In addition, the paper could mention the use of different evaluation metrics such as accuracy, precision, recall, F1-score, AUC-ROC etc to evaluate the performance of the model. Furthermore, the paper would also provide the results of the model comparing it with the traditional methods and showing the improvement in the performance of the proposed model. The results show that the proposed technique outperforms traditional methods in accurately predicting the scope and severity of the virus, and can aid in the development of more effective mitigation strategies.