PONE-D-23-27067Next-visit prediction and prevention of hypertension using large-scale routine health checkup dataPLOS ONE

Dear Dr. Wang,

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.

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Sana Sadiq Sheikh

Academic Editor

PLOS ONE

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Additional Editor Comments:

Reviewer 1:

Your study on predicting hypertension risk using XGBoost models and simulating weight reduction's impact on health indicators for hypertension prevention is commendable for its innovative approach. However, to further strengthen the credibility and applicability of your findings, I suggest considering the following:

1. Elaborate on the methodology used to determine correlations among health indicators and the rationale behind modifying specific factors during the creation of virtual visits. This will enhance the understanding of your approach.

2. Provide additional details on the statistical tests employed for validation and discuss any potential limitations in the validation process. This will ensure a more robust evaluation of the effectiveness of virtual visits.

3. Contextualize the observed changes in reclassifying individuals with high BMI after incorporating virtual visits within the dataset's overall size and clinical significance. This will help understand the practical implications of these changes for hypertension prevention.

4. Consider comparing your XGBoost models against other state-of-the-art models or risk prediction approaches. This comparative analysis would provide a broader perspective on the model's effectiveness.

5. Discuss in more detail the practical implications of your findings in clinical or preventive healthcare settings. Additionally, expand on the challenges and limitations associated with integrating features from medical imaging for further model enhancement.

6. Consider relocating legends within the graphical presentation to ensure they do not obstruct any part of the graph lines or bars. This adjustment will improve visual clarity.

7. The reasoning behind including the people count on the scale is unclear, especially considering the lack of explanation regarding the results in Figure 8 in relation to this count. Please either elucidate the results with reference to the people count or consider removing the scale and people count line for clarity.

Your study has the potential to significantly contribute to preventive healthcare strategies. Strengthening these aspects will further enhance the credibility and applicability of your research findings. Your valuable contributions to this field are appreciated, and the anticipation of advancements in your work is eagerly awaited.

Reviewer 2:

This paper uses XGBoost models to predict the occurrence of hypertension at the next patient visit. The authors employ a well-known, standard dataset. They also suggest methods of blood pressure controlling to patients with high future probability of hypertension.

Pros of the paper:

The method of training set and test construction seems justified.

The method of adding virtual visits for weight control is innovative

Cons of the paper:

The authors have not motivated the use of XGBoost models. It is true that XGBoost has become a sort of standard for machine learning application researchers. However, it has serious disadvantages as well (e.g., overfitting). The authors needed to thoroughly justify the use of XGBoost for the problem of hypertension prediction.

And the problem statement itself is also not clear to me. If the problem statement is to solve the problem of hypertension prediction, then the authors needed to try out multiple ML algorithms over multiple related datasets to justify the applicability of a single algorithm. If the problem is to solve the given dataset, then why only XGBoost? The very sudden reference to XGBoost in the first sentence of the Abstract is in fact, confusing. To me, it shows a lack of clarity as to the method of contributing to the research literature. Applying XGBoost to a problem cannot be the motivation for a paper - an algorithm is a means to achieve an end. It itself cannot be labeled as research - unless there is a strong need to do so (which is not present anywhere in the paper).

The gap analysis shown in Table 1 is weak. Summing up, the major gap is the increased length of the dataset across several dimensions. One would expect the mention of different algorithms/approaches. The second-last column seems unnecessary since selection of more features is not a concrete research gap data.

The authors have not mentioned all 266 features in the appendix

The authors have not mentioned the pros and cons of feature vector concatenation

The authors have used mean to replace missing values - this is the least preferred method unless the number of missing values to fill-in are extremely small (less than 0.5% for example). Better methods include interpolation and chained equations.

The authors have not justified the use of 5 folds.

There is no concrete machine learning methodology diagram in the whole paper.

The justification of the authors that different factors (features) do affect the final result is a very basic fact of machine learning community. This adds no value to the paper.

The ROC curve of all five folds is exactly the same - which raises doubts that a potential overfitting scenario has occurred, or the number of folds needs to increase.

Overall, a more concrete machine learning methodology needs to be applied with proper motivations and justifications.

[Note: HTML markup is below. Please do not edit.]

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: Partly

Reviewer #2: Partly

**********

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

Reviewer #1: Yes

Reviewer #2: Yes

**********

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

**********

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

**********

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: Your study on predicting hypertension risk using XGBoost models and simulating weight reduction's impact on health indicators for hypertension prevention is commendable for its innovative approach. However, to further strengthen the credibility and applicability of your findings, I suggest considering the following:

1. Elaborate on the methodology used to determine correlations among health indicators and the rationale behind modifying specific factors during the creation of virtual visits. This will enhance the understanding of your approach.

2. Provide additional details on the statistical tests employed for validation and discuss any potential limitations in the validation process. This will ensure a more robust evaluation of the effectiveness of virtual visits.

3. Contextualize the observed changes in reclassifying individuals with high BMI after incorporating virtual visits within the dataset's overall size and clinical significance. This will help understand the practical implications of these changes for hypertension prevention.

4. Consider comparing your XGBoost models against other state-of-the-art models or risk prediction approaches. This comparative analysis would provide a broader perspective on the model's effectiveness.

5. Discuss in more detail the practical implications of your findings in clinical or preventive healthcare settings. Additionally, expand on the challenges and limitations associated with integrating features from medical imaging for further model enhancement.

6. Consider relocating legends within the graphical presentation to ensure they do not obstruct any part of the graph lines or bars. This adjustment will improve visual clarity.

7. The reasoning behind including the people count on the scale is unclear, especially considering the lack of explanation regarding the results in Figure 8 in relation to this count. Please either elucidate the results with reference to the people count or consider removing the scale and people count line for clarity.

Your study has the potential to significantly contribute to preventive healthcare strategies. Strengthening these aspects will further enhance the credibility and applicability of your research findings. Your valuable contributions to this field are appreciated, and the anticipation of advancements in your work is eagerly awaited.

Reviewer #2: This paper uses XGBoost models to predict the occurrence of hypertension at the next patient visit. The authors employ a well-known, standard dataset. They also suggest methods of blood pressure controlling to patients with high future probability of hypertension.

Decision: Reject

Pros of the paper:

The method of training set and test construction seems justified.

The method of adding virtual visits for weight control is innovative

Cons of the paper:

The authors have not motivated the use of XGBoost models. It is true that XGBoost has become a sort of standard for machine learning application researchers. However, it has serious disadvantages as well (e.g., overfitting). The authors needed to thoroughly justify the use of XGBoost for the problem of hypertension prediction.

And the problem statement itself is also not clear to me. If the problem statement is to solve the problem of hypertension prediction, then the authors needed to try out multiple ML algorithms over multiple related datasets to justify the applicability of a single algorithm. If the problem is to solve the given dataset, then why only XGBoost? The very sudden reference to XGBoost in the first sentence of the Abstract is in fact, confusing. To me, it shows a lack of clarity as to the method of contributing to the research literature. Applying XGBoost to a problem cannot be the motivation for a paper - an algorithm is a means to achieve an end. It itself cannot be labeled as research - unless there is a strong need to do so (which is not present anywhere in the paper).

The gap analysis shown in Table 1 is weak. Summing up, the major gap is the increased length of the dataset across several dimensions. One would expect the mention of different algorithms/approaches. The second-last column seems unnecessary since selection of more features is not a concrete research gap data.

The authors have not mentioned all 266 features in the appendix

The authors have not mentioned the pros and cons of feature vector concatenation

The authors have used mean to replace missing values - this is the least preferred method unless the number of missing values to fill-in are extremely small (less than 0.5% for example). Better methods include interpolation and chained equations.

The authors have not justified the use of 5 folds.

There is no concrete machine learning methodology diagram in the whole paper.

The justification of the authors that different factors (features) do affect the final result is a very basic fact of machine learning community. This adds no value to the paper.

The ROC curve of all five folds is exactly the same - which raises doubts that a potential overfitting scenario has occurred, or the number of folds needs to increase.

Overall, a more concrete machine learning methodology needs to be applied with proper motivations and justifications.

**********

6. 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 #1: Yes: Muhammad Aasim

Reviewer #2: No

**********

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Attachments

Attachment

Submitted filename: Review.Dr.Tariq.Mahmood

PONE-D-23-27067R1Next-visit prediction and prevention of hypertension using large-scale routine health checkup dataPLOS ONE

Dear Dr. Wang,

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.

Your manuscript has been evaluated by both of the previous reviewers, and their comments are appended below. Both reviewers are satisfied with your revisions, and they overall recommend publication. Before we proceed, Reviewer 1 has noted that the scales used in Fig. 8 are not consistent; please revise the x-axis scale for the Random Forest subfigure. We do not anticipate that further review will be necessary.

Please submit your revised manuscript by Oct 26 2024 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.

Please include the following items when submitting your revised manuscript:

• 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 you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

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,

Hugh Cowley

Staff Editor

PLOS ONE

Journal Requirements:

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.

[Note: HTML markup is below. Please do not edit.]

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 #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

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 #1: Yes

Reviewer #2: Yes

**********

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

Reviewer #1: Yes

Reviewer #2: No

**********

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 #1: Yes

Reviewer #2: Yes

**********

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 #1: Yes

Reviewer #2: 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 #1: I am satisfied with the authors' responses, and I believe the manuscript has been significantly improved. However, I have one remaining concern regarding Figure 8. In the figure, both XGBoost and LightGBM are plotted on a scale that starts from 0 and ends at 1000, while the scale for Random Forest begins at 600 and ends at 1000. This discrepancy makes it difficult to compare Random Forest with the other two models. I recommend adjusting the scales to be consistent across all three models to facilitate a more accurate comparison. Subject to this change, I recommend the acceptance of the manuscript.

Reviewer #2: The authors have catered for all of my comments. They didn't respond to all, but the changes are there.

**********

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 #1: Yes: Muhammad Aasim

Reviewer #2: No

**********

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

Next-visit prediction and prevention of hypertension using large-scale routine health checkup data

PONE-D-23-27067R2

Dear Dr. Wang,

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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If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Hidetaka Hamasaki

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments: