Dear Dr. Tegegnie,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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We look forward to receiving your revised manuscript.

Kind regards,

Kwang-Sig Lee

Academic Editor

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?

Reviewer #1: No

Reviewer #2: Partly

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

Reviewer #1: No

Reviewer #2: No

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

The PLOS Data policy

Reviewer #1: No

Reviewer #2: Yes

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

Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: There is a huge variance in the results and the objective. The prediction method is completely omitted. Although the dataset is diverse and original from a hospital, the description of the selected features and/or labels is not provided, which raises a significant question mark about the accuracy achieved.

Reviewer #2: Did the authors explore other machine learning or deep learning methods besides XGBoost for comparison? Additionally, was the model externally validated?

The introduction and related work sections would benefit from being combined and restructured to enhance comprehension, readability, and clarity. In addition, the authors should more clearly justify the importance of this study and articulate the specific gap it aims to address.

Kindly consider revising the first sentence of the Methods section. The phrase ‘experimental, data-driven research design’ is unclear, and it would help if you clarified what is meant by this term. Specifically, is the study based on an EHR cohort or is it cross-sectional in nature?

You mean 20,960 was collected on 23/02/2025

How was CVD defined

The authors did not clearly specify which variables were included in the model, how these variables were defined, or provide supporting literature to justify their selection.

Please report the percentage of missingness for each variable. In addition, using the median to handle missing data is not an appropriate approach, as it can introduce bias. Multiple imputation methods are recommended as they provide a more robust way to address missingness. Furthermore, clarification is needed on how the median was derived from values such as 0 and 1, particularly given that the data involve disease outcomes.

This equation should be Q1 – 1.5 × IQR or Q3 + 1.5 × IQR. Author should clearly explain this.

I am not convinced that dimension reduction necessarily facilitates or improves generalizability. The authors should provide stronger justification or evidence to support this claim.

What informed standardization of category pulse rate, respiratory rate, duration of illness, and

temperature

The authors should clearly report the number of individuals available in the health record and the number ultimately included in this study, along with the eligibility criteria for inclusion.

The definition of CVD is broad; therefore, the authors should specify how CVD was defined in this study and indicate which variables were used, supported by appropriate citations.

The Methods section also requires stronger citation support and better organization.

Furthermore, it is important to clarify whether the findings can be replicated in an independent dataset, or if the model was validated in an external cohort.

Overall, the writing would benefit from improvements in clarity, flow, and logical structure.

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what does this mean? ). If published, this will include your full peer review and any attached files.

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Reviewer #1: Yes: Anil Kumar Prajapati

Reviewer #2: No

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Dear Dr. Tegegnie,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Jan 24 2026 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

• A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
• A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
• An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols . Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols .

We look forward to receiving your revised manuscript.

Kind regards,

Kwang-Sig Lee

Academic Editor

PLOS One

Journal Requirements:

If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise.

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #2: (No Response)

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

Reviewer #2: Yes

**********

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

Reviewer #2: No

**********

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

The PLOS Data policy

Reviewer #2: Yes

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

Reviewer #2: Yes

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Reviewer #2: The author did not adequately explain how CVD was defined in this study. While it might be useful for them to clarify why they used the variable CVD in the EMR, it is important that they clearly articulate whether this was based on ICD code or self-reporting.

The same applies to all clinic variables (Musculoskeletal system, neurological system, respiratory system) in this study, and they were not properly cited. If this EMR procedure has been published, it would be helpful to cite it for clarity.

Although median imputation is simple to implement, it can underestimate variability and potentially bias the estimated associations. Excluding binary outcomes from median imputation was appropriate; however, leaving missingness in the binary variables unaddressed may also introduce bias. In general, selectively imputing only certain variables can lead to inconsistencies in the analytic dataset and may bias results unless the missingness mechanism is fully justified. I recommend applying an imputation strategy (Like multiple imputation) for the outcome variable. This will help to provide a more valid, statistically principled approach to handling missing data.

While the machine learning (ML) approach used in the manuscript offers valuable predictive insights, the statistical analysis would benefit from additional rigor. I recommend that the authors include at least a bivariate analysis between the outcome variable and exposure variables or predictors before applying the ML methods. Although ML is not a traditional statistical technique, incorporating preliminary bivariate analyses is important because it allows readers to understand the basic data structure, examine the distribution of predictors across outcome categories, and identify potential associations or imbalances.

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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: No

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To ensure your figures meet our technical requirements, please review our figure guidelines: https://journals.plos.org/plosone/s/figures

You may also use PLOS’s free figure tool, NAAS, to help you prepare publication quality figures: https://journals.plos.org/plosone/s/figures#loc-tools-for-figure-preparation.

NAAS will assess whether your figures meet our technical requirements by comparing each figure against our figure specifications.

Interpretable Ensemble Machine Learning Framework for Cardiovascular Disease Prediction Using EMR Data and Large Language Models in Ethiopia

PONE-D-25-43171R2

Dear Dr. Tegegnie,

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.

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Kind regards,

Kwang-Sig Lee

Academic Editor

PLOS One

Additional Editor Comments (optional):

In my opinion, this manuscript can be published once the reference style is revised based on the Vancouver style.

Reviewers' comments: