PONE-D-21-34521Utility of machine learning in developing a predictive model for early-age-onset colorectal neoplasia using electronic health recordsPLOS ONE

Dear Dr. Hussan,

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Hsu-Heng Yen

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PLOS ONE

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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

Reviewer #3: Yes

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

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: 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: 1. The authors discussing machine learning in this paper but there isn't any clear definition about it. If the authors can provide a clear definition of machine learning and how these machine learning have been use at the introduction section that would helpful

2. As the study involved 3,116 adults aged 35-60, I would be interested to know how these sample were recruited for the study

3. Overall, the paper has demonstrated clear methodology of research

Reviewer #2: Dear Authors,

The manuscripts is well-organized and well written. The topic is interesting and significant in the current situation. I would suggest few minor revisions to enhance the quality of your manuscript.

i) You have added some supporting information. However, the graphs and charts are not clearly visible. Is it possible to upload better quality image?

ii) The references you used are good enough in quantity, however, it would be beneficial to add few more references which are up to date (preferably from 2020-2022). It will ensure the research gap.

iii) Please add the managerial and practical significance of the research in a separate paragraph.

Thank you and good luck!

Reviewer #3: I am pleased to have an opportunity to review this scientific paper about the predictive models for early-age-onset

colorectal neoplasia. I think this paper is scientifically informative and interesting. I have some comments and suggestions as below.

1.Major

The author should add the work plot in this manuscript and it's necessary to add the inclusion and exclusion criteria in this plot.

The figure legend and table should put in the end of manuscript.

You should put the P value in your figure.

The history of colorectal neoplasia in family member may be added in this manuscript, which was an important factor associated with colorectal cancer.

Patients with abnormal laboratory studies (CEA and CA199) should be excluded in this manuscript which may result a bias of the cohort.

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Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: Yes: Syed Far Abid Hossain

Reviewer #3: No

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Utility of machine learning in developing a predictive model for early-age-onset colorectal neoplasia using electronic health records

PONE-D-21-34521R1

Dear Dr. Hussan,

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,

Hsu-Heng Yen

Academic Editor

PLOS ONE

 Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #2: All comments have been addressed

Reviewer #3: All comments have been addressed

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2. 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 #2: Yes

Reviewer #3: Yes

**********

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

Reviewer #2: Yes

Reviewer #3: Yes

**********

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

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #2: Yes

Reviewer #3: Yes

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

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #2: Yes

Reviewer #3: Yes

**********

6. 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 #2: The revised version is good. Thank you for addressing the comments properly. The manuscript is well organized now.

Reviewer #3: (No Response)

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7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #2: Yes: Syed Far Abid Hossain, Coordinator, MBA Program, IUBAT

Reviewer #3: No