Dear Dr. Afrashteh,

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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Please submit your revised manuscript by Apr 11 2025 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.

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We look forward to receiving your revised manuscript.

Kind regards,

Junzheng Yang

Academic Editor

PLOS ONE

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3. Thank you for stating the following financial disclosure:

“I.N. and B.L. acquired funding for the BEH program. The BEH Program received funding from the Persian Gulf Biomedical Sciences Research Institute, which is affiliated with Bushehr University of Medical Sciences (https://bpums.ac.ir), and the Endocrinology and Metabolism Research Institute, affiliated with Tehran University of Medical Sciences (https://tums.ac.ir). Researchers from both institutions collaborated in designing and implementing this study.”

Please state what role the funders took in the study.  If the funders had no role, please state: "The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript."

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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: Yes

Reviewer #2: No

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

Reviewer #1: Yes

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: Yes

Reviewer #2: No

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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: 1. The manuscript successfully identifies OSA as a significant public health issue associated with cardiovascular comorbidities such as hypertension, diabetes, and myocardial infarction. However, the study could benefit from explicitly linking these findings to the broader epidemiological context of sleep apnea in the target population. The authors can include a brief discussion on the prevalence of OSA and its associated cardiovascular risks in your study population, possibly using existing regional data as a reference.

2. The use of standardized tools like the RAQ is commendable, but it would be valuable to confirm whether these tools were validated for your specific population or if local adaptations were made. Additionally, while the measurement of grip force and anthropometric variables is appropriate, it would enhance the methodology section to explicitly mention any validation studies or interobserver reliability assessments for these measures. The authors may cite any validated scales or tools used in this study and provide references to their validation studies.

3. The manuscript provides a comprehensive overview of data collection procedures, including medical history, physical examinations, and questionnaires. However, the sample size is not clearly specified. To ensure generalizability, it would be helpful to clarify whether this study was part of a larger prospective or retrospective cohort and, if applicable, provide demographic details about your participants (e.g., age, gender distribution, education level, etc.). The authors are suggested to specify whether this study is cross-sectional or part of a larger longitudinal investigation. If applicable, include basic demographics of the study population to enhance external validity.

4. The presentation of results is clear and organized, with mean values provided for key variables like blood pressure, grip force, and anthropometric measurements. However, some of the p-values (e.g., <0.001) are not interpreted in the context of other studies. This could limit readers' understanding of the statistical significance relative to previous findings. The authors should provide a brief discussion of how the observed results compare to those in prior studies on OSA and cardiovascular risk factors. For example, reference studies that report similar p-values for blood pressure measurements or grip force in sleep apnea patients.

5. While the manuscript provides valuable insights into the relationship between OSA and cardiovascular comorbidities, there is no explicit discussion section to contextualize these findings within the broader scientific literature. A brief discussion linking your results to prior research would greatly enhance the manuscript's impact. The authors may include a discussion where you compare your findings to existing studies on sleep apnea and its complications, focusing on similarities, differences, and potential mechanisms.

6. The conclusion is concise but could be improved by summarizing the key findings without repeating previous results or data. Additionally, it would be beneficial to highlight the implications of your study for clinical practice and public health strategies. In the conclusion, the authors may explicitly state how your findings can inform clinical decisions, such as recommending polysomnography for high-risk patients or implementing targeted intervention programs.

Reviewer #2: 1. How were missing values and outliers in the medical records handled prior to model training?

2. Were any normalization or scaling techniques applied to the predictor variables?

3. The abstract is not coherent. It would be good if authors can write a sentence describing numerical results and improvement over other methods.

4. The complexity of the proposed model and the model parameter uncertainty are not enough mentioned.

5. Discussions" section should be added in a more highlighting, argumentative way. The author should analysis the reason why the tested results is achieved.

6. Authors should pattern the motivation behind using this method to explain in the introduction.

7. There needs to be citation of recent papers on this topic and revise the literature section with slight Incorporation of recent ideas, for e.g.,

- A resource-aware multi-graph neural network for urban traffic flow prediction in multi-access edge computing systems

- Adaptive fuzzy backstepping secure control for incommensurate fractional order cyber–physical power systems under intermittent denial of service attacks

- Fuzzy adaptive control for consensus tracking in multiagent systems with incommensurate fractional-order dynamics: Application to power systems

- Revolutionizing E-Commerce With Consumer-Driven Energy-Efficient WSNs: A Multi-Characteristics Approach

8. Could you elaborate on the hyperparameter optimization process for each machine learning algorithm?

9. Was grid search or another optimization method used to fine-tune model parameters?

10. Which features emerged as the most critical across different models, and how consistent were these findings?

11. Did you observe any differences in feature importance when comparing the performance of individual classifiers versus ensemble models?

12. Parameters of network have been enhanced using training data "until the model obtains the maximum accuracy". If this accuracy is the training accuracy, maybe over-fitting has been performed. If this accuracy is the testing accuracy, the system is adjusted over the same subset that is evaluated. A validation subset could be used to optimize the system with different data than the testing data and without performing over-fitting. In addition, it would be interesting to know which range of each parameter has been analyzed."?

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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: No

Reviewer #2: Yes:  Nill

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Dear Dr. Afrashteh,

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 Aug 04 2025 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,

Junzheng Yang

Academic 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

Reviewer #3: (No Response)

Reviewer #4: (No Response)

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

Reviewer #3: Yes

Reviewer #4: Yes

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

Reviewer #3: Yes

Reviewer #4: Yes

**********

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

The PLOS Data policy

Reviewer #3: Yes

Reviewer #4: No

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

Reviewer #3: Yes

Reviewer #4: Yes

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Reviewer #3: The revised manuscript titled “Machine learning-based predictive modeling of angina pectoris in an elderly community-dwelling population: results from the PoCOsteo study” is a timely and relevant contribution that addresses the growing need for predictive analytics in geriatric cardiovascular care using machine learning (ML) methodologies. The authors have revised the manuscript thoroughly and addressed the previous queries raised, including ethical clarifications, data availability statements, and funder roles. Below are my detailed comments:

Strengths:

- The focus on angina pectoris prediction among an elderly population using ML models is novel and valuable, particularly in preventive public health and precision medicine contexts.

- The use of a wide array of machine learning algorithms (from interpretable models like logistic regression and LDA to more complex ones like random forest and AdaBoost) reflects commendable methodological breadth. The incorporation of hyperparameter tuning, ten-fold cross-validation, and permutation feature importance enhances the robustness of the analysis.

Suggestions for Minor Revisions:

- While the results are well-structured, it would enhance clarity if the authors specify confidence intervals along with AUC values for the top-performing models to better assess performance stability.

- Although the limitations are generally implied, an explicit limitations paragraph would strengthen the discussion. This could include the retrospective nature of the study, lack of external validation, or potential overfitting risks in ensemble models despite cross-validation.

- The permutation feature importance analysis reveals insightful predictors such as thrombotic vascular diseases and anemia. It would be valuable to comment briefly on how these findings align (or contrast) with existing literature on cardiovascular risk in the elderly.

- The manuscript is overall well-written, but a few minor typographical and grammatical adjustments may further improve readability. For instance, in the Abstract: "We aim to develop" may be better stated in the past tense ("We aimed to develop") given the retrospective design.

- While this is provided in response to editorial comments, ensure this data availability statement is also clearly reflected in the final manuscript version in the appropriate section.

This is a solid and promising manuscript that makes a meaningful contribution to the application of machine learning in cardiovascular risk prediction. With the minor revisions mentioned above, it will be suitable for publication in PLOS ONE.

Reviewer #4: This manuscript addresses the prediction of angina pectoris among elderly community-dwelling individuals using various machine learning (ML) methods. Utilizing data from the PoCOsteo study, the authors compared multiple ML algorithms (LR, MLP, SVM, KNN, LDA, QDA, DT, RF, AdaBoost) to identify robust predictors and evaluate their predictive performance. Angina was classified using the standardized Rose Angina Questionnaire (RAQ).

Generally great study, its honor to correspond as a reviewer.

After reading the whole text i have some minor questions:

1) Can you provide detailed rationale and\or discussion on chosen imputation methods (SimpleImputer, mean/most frequent). Did alternative methods get considered, and if so why were they rejected?

2) Additional dedicated discussion on how your predictive models could be integrated into routine clinical workflows or decision-support systems may streaighten up the final conclusion of your study.

3) Also is it possible to strengthen the discussion by comparing your results more explicitly with recent similar studies involving ML models in cardiovascular prediction.

* Minor grammatical refinements are recommended to enhance clarity and readability. An additional round of proofreading is suggested.

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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 #3: Yes:  Amaan Arif

Reviewer #4: No

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[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

Machine learning-based predictive modeling of angina pectoris in an elderly community-dwelling population: results from the PoCOsteo study

PONE-D-24-53747R2

Dear Dr. Afrashteh,

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.

An invoice will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. Please make sure your user information is up-to-date by logging into Editorial Manager at Editorial Manager®  and clicking the ‘Update My Information' link at the top of the page. If you have any questions relating to publication charges, please contact our Author Billing department directly at authorbilling@plos.org.

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,

Junzheng Yang

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #3: All comments have been addressed

Reviewer #4: All comments have been addressed

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

Reviewer #3: Yes

Reviewer #4: Yes

**********

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

Reviewer #3: Yes

Reviewer #4: Yes

**********

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

The PLOS Data policy

Reviewer #3: Yes

Reviewer #4: Yes

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

Reviewer #3: Yes

Reviewer #4: Yes

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Reviewer #3: The revised manuscript titled "Machine learning-based predictive modeling of angina pectoris in an elderly community-dwelling population: results from the PoCOsteo study" presents an important and timely contribution to the field of predictive modeling in cardiovascular health.

Strengths:

- The authors have successfully developed and validated machine learning (ML) models to predict angina pectoris in elderly populations using robust methods (e.g., 10-fold cross-validation, hyperparameter tuning).

- The choice to include both interpretable models (LDA, LR) and complex ensemble models (RF, AdaBoost) is commendable and allows for a balance between accuracy and interpretability.

- The revised version addresses the previous reviewers' concerns effectively by:

1. Including confidence intervals for AUC values.

2. Adding an explicit limitations section.

3. Contextualizing key predictors with existing cardiovascular literature.

4. Clarifying the rationale for chosen imputation methods.

5. Suggesting pathways for clinical integration of predictive tools.

- The study appears technically sound. The dataset (n=2000) is substantial, and model performance metrics (AUC, feature importance) are well-documented. Feature selection and permutation importance analyses further support the validity of the findings.

- While the dataset is not publicly available due to ethical restrictions, the authors have provided a pathway for data access through institutional review. The ethics approval and waiver of consent are adequately reported.

Reviewer #4: The authors have thoroughly and carefully addressed all the concerns and suggestions raised during the initial review process. Each point raised previously has been adequately clarified, and the necessary modifications have been successfully implemented in the revised manuscript. The paper now demonstrates significantly improved clarity, methodological transparency, and overall coherence.

Given these substantial improvements, I am confident that the manuscript now aligns with and fulfills the rigorous scientific standards and expectations upheld by PLOS ONE. It represents a robust contribution to the field, with findings clearly communicated, methodological approaches justified, and results effectively discussed in the context of existing literature.

I have no further comments or reservations regarding the content or quality of the manuscript and strongly recommend it for publication.

It has been an honor and privilege to serve as a reviewer for this manuscript, and I thank the editors for the opportunity to contribute to the publication process.

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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 #3: No

Reviewer #4: Yes:  Vladislav Rublev

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