Application of a transparent artificial intelligence algorithm for US adults in the obese category of weight

Alexander A. Huang; Samuel Y. Huang

doi:10.1371/journal.pone.0304509

Peer Review History

Original SubmissionAugust 2, 2023
2 Oct 2023 Decision Letter - Aleksandra Klisic, Editor PONE-D-23-24436Application of a transparent artificial intelligence algorithm for US adults in the obese category of weightPLOS ONE Dear Dr. Huang, 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 Nov 16 2023 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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The PLOS ONE style templates can be found at https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf [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 ******** 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: This is a study designed to perdict the obese category of weight and investigate its relationships with factors, ultimately contributing to healthier lifestyle choices and timely management of obesity. The study uses questionnaires that included demographic, dietary, exercise and health information from the US National Health and Nutrition Examination Survey (NHANES 2017 – 2020) who had BMI 30 or higher defined as obesity. This is an interesting study. However, I have two questions. First, the author should address the association between blood lead levels and obesity. Besides, the author should provide a verification result regarding this algorithm. Reviewer #2: In this study, the authors demonstrated the accurate prediction of the obesity category using questionnaires from the US National Health and Nutrition Examination Survery. However, further clarification and revision of the descriptions are needed for publication as follows. First, other machine learning methods, other than XGBoost, need to be investigated to validate the results. Second, the authors need to reorganize the results to make them easier for readers to understand. Please see the following comments to the authors. Major comments Methods 1. The authors only used XGBoost to develop the prediction model. Please clarify the rationale for using this model in this study. Also. it would be reasonable to use other machine learning models to validate the results. 2. The authors performed the univariate analysis for each variable. Please provide the name of the statistical analysis for clarification. Results 1. The authors express the variation of each variable using SD. Does this mean that all variables follow a parametric pattern? If not, please express some of the variables in a non-parametric way. 2. Table1 contains so many variables that the readers would be exhausted. The authors express the same variable in a different unit. Please consider choosing either of the units so that the table would be more concise. 3. Please add the confidence interval to the value of diagnostic accuracy. 4. Figure 2 contains so much information that readers would be confused to interpret the significance of the figure. The lower order of the variables in the SHAP analysis should have less contribution to the model development. Please delete the lower order of the variables to make it more concise. Some of the results could be moved to Supplementary Material. ****** 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: No 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. https://doi.org/10.1371/journal.pone.0304509.r001
Revision 1
20 Mar 2024 Author Response We would like to extend our sincere gratitude to the reviewers for their insightful comments and constructive criticisms. Their detailed feedback has been invaluable in guiding our revisions, allowing us to address critical aspects of our methodology and analysis that required further clarification and enhancement. The reviewers’ expertise and thoughtful suggestions have significantly contributed to the depth and rigor of our study, ensuring a more comprehensive and robust examination of the predictive models utilized in the context of insomnia prediction. Their contributions have not only facilitated methodological improvements but also enriched our discussion, leading to a more nuanced understanding of the complexities involved in variable selection within machine learning and regression models. We are grateful for the opportunity to refine our work through this collaborative and iterative process, which has undeniably strengthened the quality and impact of our research. We have responded to all reviewer feedback to the best of our abilities. 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: This is a study designed to perdict the obese category of weight and investigate its relationships with factors, ultimately contributing to healthier lifestyle choices and timely management of obesity. The study uses questionnaires that included demographic, dietary, exercise and health information from the US National Health and Nutrition Examination Survey (NHANES 2017 – 2020) who had BMI 30 or higher defined as obesity. This is an interesting study. However, I have two questions. First, the author should address the association between blood lead levels and obesity. Besides, the author should provide a verification result regarding this algorithm. The authors have addressed both of these comments in the paper. First – the blood lead levels and obesity can be associated with socioeconomic status, however, further analysis must be made to understand if this is the correlative third-variable problem. It is difficult to exactly explain away each of the covariates as this is an unbiased data-driven approach rather than one leading to direct discovery of causal linkages. Additionally, verification of the algorithm was attempted through the train-test sets and multiple models executed within this paper. Further research is needed to address this. Reviewer #2: In this study, the authors demonstrated the accurate prediction of the obesity category using questionnaires from the US National Health and Nutrition Examination Survery. However, further clarification and revision of the descriptions are needed for publication as follows. First, other machine learning methods, other than XGBoost, need to be investigated to validate the results. Second, the authors need to reorganize the results to make them easier for readers to understand. Please see the following comments to the authors. Major comments Methods 1. The authors only used XGBoost to develop the prediction model. Please clarify the rationale for using this model in this study. Also. it would be reasonable to use other machine learning models to validate the results. 2. The authors performed the univariate analysis for each variable. Please provide the name of the statistical analysis for clarification. 1) The authors have utilized XGBoost based upon its frequency in the machine-learning literature. Additionally, the authros tested multiple different models before choosing xgboost as its final one. The XGBoost model was chosen as it was the strongest. These comments have ben added to the manuscript. 2) The authors have added the name of the statistical analysis into the paper. Results 1. The authors express the variation of each variable using SD. Does this mean that all variables follow a parametric pattern? If not, please express some of the variables in a non-parametric way. (Yes, all variables followed a parametric pattern) 2. Table1 contains so many variables that the readers would be exhausted. The authors express the same variable in a different unit. Please consider choosing either of the units so that the table would be more concise.(The authors recognize the table-1 is large, however, it is meant for completeness, and different aspects of the table may be of interest to different readers) 3. Please add the confidence interval to the value of diagnostic accuracy.(Confidence intervals added throughout the paper) 4. Figure 2 contains so much information that readers would be confused to interpret the significance of the figure. The lower order of the variables in the SHAP analysis should have less contribution to the model development. Please delete the lower order of the variables to make it more concise. Some of the results could be moved to Supplementary Material. (This comment is very accurate! We recognize that adding too many variables clutters reading. We recognize these concerns for conciseness of the figure – we include the figure 2 as is since we believe that using human judgment for what contributes an important variable may lead to bias – thus we utilize only algorithmic methodology for variable selection. Attachments Attachment Submitted filename: Reviewer Commments Obesity.docx https://doi.org/10.1371/journal.pone.0304509.r002
14 May 2024 Decision Letter - Aleksandra Klisic, Editor Application of a transparent artificial intelligence algorithm for US adults in the obese category of weight PONE-D-23-24436R1 Dear Dr. Huang, 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. 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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: 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 #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: This is a machine learning study to verify the usefulness of XGBoost to predict the obese category of weight and also the use of Shapely Additive Explanations (SHAP) on the various covariates and their feature importance. The machine learning model had an Area under the receiver operator curve of 0.8295 and the top four covariates were waist circumference , GGT , platelet count,and AST. These models successfully identify crucial risk factors associated with the obese category of weight. This exciting study revealed several factors that art not considered relevant to obesity before. Some relevant factors (CRP, insulin) are more likely to relate to the chronic inflammation status within obese people. This is a study with innovation and I recommend publication. Reviewer #2: The authors have adequately revised the manuscript according to the reviewers' recommendations. This version of the manuscript seems suitable for publication. ****** 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: Kuo-Ching YUAN Reviewer #2: Yes: Takehiko Oami ******** https://doi.org/10.1371/journal.pone.0304509.r003
Formally Accepted
20 May 2024 Acceptance Letter - Aleksandra Klisic, Editor PONE-D-23-24436R1 PLOS ONE Dear Dr. Huang, I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team. At this stage, our production department will prepare your paper for publication. This includes ensuring the following: * All references, tables, and figures are properly cited * All relevant supporting information is included in the manuscript submission, * There are no issues that prevent the paper from being properly typeset If revisions are needed, the production department will contact you directly to resolve them. If no revisions are needed, you will receive an email when the publication date has been set. At this time, we do not offer pre-publication proofs to authors during production of the accepted work. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few weeks to review your paper and let you know the next and final steps. Lastly, if your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. 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. If we can help with anything else, please email us at customercare@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Dr. Aleksandra Klisic Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0304509.r004

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