PONE-D-24-05709

Metabolic Syndrome Predictive Modeling in Bangladesh applying Machine Learning Approach

PLOS ONE

Dear Dr. Hossain,

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Donovan Anthony McGrowder, PhD., MA., MSc

Academic Editor

PLOS ONE

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Additional Editor Comments:

Dear Dr. Hossain,

Your manuscript “Metabolic Syndrome Predictive Modeling in Bangladesh applying Machine Learning Approach” 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 given are below.

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

 Best regards,

Dr. Donovan McGrowder

[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: 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: Abstract:

- The abstract should clearly state the main objectives, methodology, key results, conclusions and limitations of the study. Currently it only summarizes the background.

- Include the key risk factors identified, prevalence of MetS found, and main ML model utilized in the results.

Introduction:

- Expand more on the rationale for identifying risk factors and predicting MetS prevalence in the Bangladeshi population specifically.

- after ''and working in irregular shifts[7].'' discuss the role in particular of obstructive sleep apnea. cite doi:10.3390/life13030702.

- Provide more background on MetS diagnostic criteria and associated health risks to establish significance.

Methods:

- Describe the National STEPS Survey sampling methodology, data collection procedures, and variables obtained in more detail.

- Explain how MetS was defined - which diagnostic criteria used.

- Specify why certain variables were categorized and encoding approaches for ML models.

- State specific ML models, parameters, evaluation metrics, and statistical analysis done.

Results:

- Focus this section only on key results - prevalence, risk factors, model performance. Remove general discussion.

- Include quantitative performance metrics for each ML model - precision, recall, ROC, etc.

Discussion:

- Limitations should note small subset of survey data used, exclusion of potentially relevant variables like lipids. cite doi:10.1016/j.compbiomed.

- Discussion of inflammation markers and future studies seems speculative without any results presented. Would remove.

- Avoid overstating conclusions on predictive capabilities of models until externally validated.

Reviewer #2: Dear authors,

I have now completed the review of the manuscript titled "Metabolic Syndrome Predictive Modeling in Bangladesh applying Machine Learning Approach".

The author aims to predict the prevalence of Metabolic Syndrome (MetS) in Bangladesh using various machine learning algorithms. The study analyzes data from 8185 participants, identifying key risk factors for MetS and assessing the performance of different models in predicting the condition.

The manuscript is interesting and, in general, fairly well-written.

I have some suggestions to further improve the quality of the manuscript.

I would like to suggest that the authors address these limitations in the article, either by discussing them in the limitations section or, where feasible, by making the appropriate revisions:

1. It's crucial to assess how well the models generalize to unseen data. Cross-validation techniques and external validation on datasets from different populations could enhance the robustness of the findings.

2. The study could benefit from a direct comparison with existing predictive models for MetS to highlight its contributions or improvements.

3. While the study employs feature selection techniques, further exploration into dimensionality reduction methods could improve model performance and interpretability.

4. The exclusion of certain variables deemed insignificant could potentially overlook complex interactions between features that contribute to MetS. A more detailed analysis or justification for the exclusion of these variables might provide deeper insights.

5. The practical applicability of the models in clinical settings is not extensively discussed. Future work could focus on how these models can be integrated into healthcare systems for early detection and intervention strategies.

6. Mention breifly deep learning methods article and add in the reference. For example: "An Adaptive Ensemble Deep Learning Framework for Reliable Detection of Pandemic Patients", "Deep Learning Network Selection and Optimized Information Fusion for Enhanced COVID-19 Detection". Deep learning could offer insight into the future research direction.

Thank you for your valuable contributions to our field of research. I look forward to receiving the revised manuscript.

Reviewer #3: In this study, Hossain et al. perform machine learning to identify risk factors for metabolic syndrome. The topic is interesting, and data support the authors’ conclusions. The story is clear; however, the method section is incomplete. The authors write general information of procedures, such as what sensitivity is or how specificity is calculated. This basic and general information is not necessary. The authors should write procedures the authors performed in this study, but no information is provided in this manuscript, which is not appropriate. The authors should provide more detailed information of procedures, such as R and its version, packages, parameters used in analysis, etc.

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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: Antonino

Reviewer #2: No

Reviewer #3: No

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Metabolic Syndrome Predictive Modeling in Bangladesh applying Machine Learning Approach

PONE-D-24-05709R1

Dear Dr. Hossain,

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,

Aleksandra Klisic

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

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

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 #1: Yes

Reviewer #2: Yes

Reviewer #3: (No Response)

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

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: (No Response)

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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

Reviewer #3: (No Response)

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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 #1: Yes

Reviewer #2: Yes

Reviewer #3: (No Response)

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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: The manuscript Is improved and the Authors performed all the revisions required. I can suggest tò accept.

Reviewer #2: All comments were addressed. I declare no further comments to be included. Thank you for considering my opinion.

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 #1: Yes: None

Reviewer #2: No

Reviewer #3: No

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