PONE-D-22-32680Use of Machine Learning to Identify Risk Factors for Coronary Artery DiseasePLOS ONE

Dear Dr. Huang,

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Academic Editor

PLOS ONE

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Additional Editor Comments (if provided):

Dear Dr. Huang,

Your manuscript “Use of Machine Learning to Identify Risk Factors for Coronary Artery Disease” has been assessed by our reviewers. They have raised a number of points which we believe would improve the manuscript and may allow a revised version to be published in PLOS ONE. Their reports, together with any other comments, are below.

 If you are able to fully address these points, we would encourage you to submit a revised manuscript to PLOS ONE.

 Best regards,

Dr. Donovan McGrowder

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available?

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Reviewer #1: Yes

Reviewer #2: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

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Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: General comments

Integrating the Machine-learning model with the usual statistical analysis model can be a promising revolution in e-Health ad m-Health to solve complex issues accurately and effectively.

The crux of this manuscript is the use of new analytical techniques to complement the usual statistical model. The authors have used machine learning to predict the risk of CAD by capturing the actual physiological relationship of the factors associated with the risk of CAD. The manuscript is well written: methodology is well narrated and executed; the results are well presented, and the discussion is well articulated.

The manuscript lacks page numbers; please insert them in the next version of the manuscript.

Specific comments

Abstract:

- Line 24: The author should define the abbreviation “SHAP.” Most abbreviations must be defined upon first use unless otherwise explained.

Methods:

- Line 91: Why p<0.0001 was the cut-off level of statistical significance to include covariate in the machine-learning model? Is there any reference or statistical explanation?

Discussion:

- Line 200: Misspelled word - Replace “contrary” artery disease with “coronary” artery disease.

Reviewer #2: Thanks very much for this very interesting and informative article. I feel that overall the paper was well written and without significant flaws. The study objectives and the methods section are clearly defined, the article is easily readable, and the topic is relevant to the readership. Limitations are adequately addressed. Conclusions are appropriate for the scope of the study. The paper is formally correct and it is clear its clinical relevance, and what this article should add to the body of knowledge on this topic.

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Use of Machine Learning to Identify Risk Factors for Coronary Artery Disease

PONE-D-22-32680R1

Dear Dr. Huang,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Donovan Anthony McGrowder, PhD., MA., MSc

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Dear Dr. Huang,

The manuscript entitled “Use of Machine Learning to Identify Risk Factors for Coronary Artery Disease” was revised in accordance with the reviewers’ comments and is provisionally accepted pending final checks for formatting and technical requirements.

Regards,

Dr. Donovan McGrowder (Academic Editor)