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Based on the previous reviews, the manuscript was accurately revised. Also, the statistical analysis performed is adequate to the study.
[# PeerJ Staff Note - this decision was reviewed and approved by Xiangjie Kong, a PeerJ Section Editor covering this Section #]
The figures are not readable - please provide legible versions.
The work under review estimates the risk during COVID. Generally speaking, the research is interesting and correlates the outcomes and the treatment.
The experiments are well-planned and conducted.
The findings might be interesting for the public.
I think the quality of the figures is low.
Please consider the reviewers comments.
**PeerJ Staff Note:** Please ensure that all review and editorial comments are addressed in a response letter and that any edits or clarifications mentioned in the letter are also inserted into the revised manuscript where appropriate.
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I did not identify any new features, or open or resolved problems, nor are the scientific contributions clear. Computationally, which metrics, attributes, and criteria were improved about what already exists?
The criteria used, the parameters, and the results obtained are not clear.
The criteria used, the parameters, and the results obtained are not clear.
I strongly suggest that authors review the related works section to better clarify the criteria used. The implementation carried out, the tests, and the results obtained are also not clear how they arrived at the values, in short, the topic is interesting but there are several gaps.
The authors propose a novel scheme for leveraging machine learning (ML) methodologies to predict the likelihood of mortality in infectious diseases like COVID-19 based on blood test data. They identify five highly impactful features – age, LDH, lymphocytes, neutrophils, and hs-CRP – which, when combined, achieve an impressive 96% accuracy in predicting mortality. By integrating XGBoost feature importance with neural network classification, the optimal method further elevates the predictive power by achieving exceptional accuracy and 90% precision in mortality prediction for infectious diseases, up to 16 days before the event. The authors demonstrate the model's robust performance and practical applicability through testing with three instances based on varying timeframes leading to the outcome. Although promising, the approach necessitates further enhancements before it can be fully embraced.
Choice of five features: The paper does not explicitly explain the choice of five specific features. Please justify their selection or suggest relevant literature that highlights the significance of these features.
Missing key contributions: The introduction lacks clarity on the study's specific contributions. Please mention them explicitly early on.
Literature review: A table comparing the proposed approach with existing literature would be beneficial for clarity and impact.
Number of biomarkers: 74 biomarkers might be insufficient for a comprehensive evaluation. Authors should justify their choice or explore adding more relevant markers.
Data set characteristics: The description of "Name, age, gender, address, contact information, etc." is imprecise. Authors should provide a detailed list of all data set characteristics, including classes, to avoid ambiguity.
Data set table: A table summarizing key insights about the data set, including classes, would be valuable.
Figure quality and results discussion: Poor figure quality and insufficient result discussion weaken the paper's persuasiveness. The authors should improve figure quality and explicitly discuss each result, highlighting how their scheme surpasses existing studies in specific areas.
By addressing these points and implementing the suggested improvements, the authors can significantly strengthen their research and increase its potential for acceptance.
Included in Basic Reporting
Included in Basic Reporting
None
The work under review concerns the implementation of machine learning in rapid data processing of the COVID tests. The work is well written, readable and might be interesting for many different professionals and non-professionals.
The work is well-planned and the results are carefully presented.
The results are reasonable and prove the idea of the authors.
However:
- References are not cited in increasing order.
- The work needs additional editing. For example, there are wrongly used capital characters, missing punctuation, not well edited captions, part of the sentences are long and have might not be understandable or have wrong word order etc.
- The quality of the figures is low.
- The caption of Figure 5 is missing.
- The positions of the figures is not shown in the work.
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