Towards Machine Learning Algorithms in Predicting the Clinical Evolution of Patients Diagnosed with COVID-19

Round 1

Reviewer 1 Report

The performance of various classification algorithms applied to the same data set, which contains data gathered from patients diagnosed with COVID-19 and registered in the Influenza Epidemiological Surveillance System, SIVEP, was evaluated and validated by the authors using a comparative analysis using benchmarking techniques. Although the quality of the work is good, there are still a few problems that need to be resolved before publishing. Here are my observations:

1-Include the entire statement before using any abbreviations. The document needs to stand alone.

2-The abstract section must be free of the paper's organization.

3- Many important references are missing from the introduction section, and this section lacks adequate citations. It is preferable to cite recent publications:

https://link.springer.com/article/10.1007/s00521-022-07424-w

https://www.sciencedirect.com/science/article/pii/S0010482522002530

 https://www.sciencedirect.com/science/article/pii/S0010482521009355

4-The paper's structure is poor. particularly the introduction section,  Combine short sentences to create complete paragraphs.

5-Please discuss the work's implications in the conclusion section.

Author Response

Dear Reviewer

Thank you so much for allowing revision to our paper. We have given the response to the reviewer’s comments and suggestions below. Besides, we also revised everything suggested carefully in the manuscript. We believe that the manuscript is ready for publication now. Thank you so much for your assistance.

Sincerely,

Authors

Author Response File: Author Response.pdf

Reviewer 2 Report

In this manuscript, the authors propose a comparative analysis using benchmarking techniques to evaluate and validate the performance of some classification algorithms applied to the same data set, which contains information collected from patients diagnosed with COVID-19,  registered in the Influenza Epidemiological Surveillance System, SIVEP. With this approach, 30,000 (thirty thousand) cases were analyzed during the training and testing phase of the prediction models. This work proposes a comparative approach of machine learning algorithms, working on the knowledge discovery task to predict clinical evolution in patients diagnosed with COVID-19. Adequate revisions to the following points should be undertaken to justify the recommendation for publication.

·       This paper has more than spelling and grammatical errors. Please fix all of them.

·       The abstract section is fragile. Please re-write an abstract section, justify an obtained result and contribution, improve a proposed method, etc.

·       The authors should clearly state the limitations of the proposed method in other applications.

·       The flowchart of the proposed method is missed; please drow it.

·       All figures have low quality, and please improve all of them.

·        Please use a new comparison algorithm, such as the Farmland fertility algorithm, African Vultures Optimization Algorithm, and Artificial Gorilla Troops Optimizer.

·        In the related work, much research is missed in this manuscript. Such as Feature Selection using Artificial Gorilla Troop Optimization for Biomedical Data: A Case Analysis with COVID-19 Data,  The application of K-means clustering for province clustering in Indonesia of the risk of the COVID-19 pandemic based on COVID-19 data , etc.

·         The citations in “Other reported symptoms 117 include, but are not limited to, diarrhoea, dizziness, sore throat, abdominal pain, anorexia, 118 and vomiting [2], [6], [7].” are improper; citing so many papers once is not professional.

·       Please change the title section (4. Results) to (4. Results and Discussion), and write the discussion.

·        Please change the title of the end section (7. Conclusions, Future Work, and Research Limitations 479) to (7-Conclusions and Future Works), and write some future works.

 

Good luck

Author Response

Dear Reviewer

Thank you so much for allowing revision to our paper. We have given the response to the reviewer’s comments and suggestions below. Besides, we also revised everything suggested carefully in the manuscript. We believe that the manuscript is ready for publication now. Thank you so much for your assistance.

Sincerely,

Authors

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

It can be accepted.

Reviewer 2 Report

  I think the paper has been well revised according to the previous reviewers, and the current version of the manuscript is acceptable for publication.