-->PDIG-D-25-00724-->-->Predicting Vaping Cessation in Young Adults: A Machine Learning and Explainable Artificial Intelligence (XAI) Approach to Public Health Intervention.-->-->PLOS Digital Health-->--> -->-->Dear Dr. Mahajan,-->--> -->-->Thank you for submitting your manuscript to PLOS Digital Health. After careful consideration, we feel that it has merit but does not fully meet PLOS Digital Health'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 Apr 10 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 digitalhealth@plos.org. 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-->Comments to the Author

1. Does this manuscript meet PLOS Digital Health’s publication criteria? Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe methodologically and ethically rigorous research with conclusions that are appropriately drawn based on the data presented.? Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe methodologically and ethically rigorous research with conclusions that are appropriately drawn based on the data presented.-->

Reviewer #1: Yes

Reviewer #2: Partly

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

Reviewer #1: No

Reviewer #2: Yes

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The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception. 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.requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception. 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: No

Reviewer #2: No

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

PLOS Digital Health 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

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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: attached

Reviewer #2: Review Comments to the Author

This manuscript explores the use of machine learning and explainable AI to predict vaping cessation among young adults, addressing a highly relevant public health challenge. The topic is well aligned with PLOS Digital Health, and the authors demonstrate familiarity with a broad range of ML techniques and contemporary cessation literature. Ethical approval and participant protections are appropriately described.

That said, several major issues should be addressed before the manuscript can be considered for publication:

The abstract should be rewritten in a structured format consistent with PLOS Digital Health standards. Specifically, the authors should clearly separate and label the following components: Background, Objective, Methods, Results, and Conclusions. The current abstract is narrative and overly dense, making it difficult to quickly identify the study aim, analytic approach, and key findings. A structured abstract would substantially improve clarity, accessibility, and alignment with journal expectations, particularly for a digitally focused and methodologically complex study.

Language: The manuscript is generally intelligible and written in standard academic English. However, substantial editorial refinement is needed: Repetition across the Introduction and Discussion reduces clarity and conciseness. Some sections (especially Results and Discussion) are overly long and descriptive, with limited synthesis. Terminology is occasionally inconsistent (e.g., cessation definitions, model performance claims).

Minor grammatical errors, formatting inconsistencies, and typographical issues are present.

Outcome Definition and Study Design: The primary outcome of “vaping cessation” is insufficiently defined. It is unclear whether cessation reflects a quit attempt, short-term abstinence, or sustained cessation, and over what timeframe. This ambiguity undermines interpretation of model performance and clinical relevance.

Sample Size and Model Complexity

The relatively small sample (n = 119) is a major limitation given the number of predictors and the use of complex models (Random Forest, SVM, XAI). The manuscript should explicitly discuss risks of overfitting, limited generalizability, and instability of feature importance rankings.

Statistical and ML Methodology: While multiple models are explored, the rationale for using overlapping feature-selection strategies (forward/backward selection, Boruta, LASSO) is unclear. Performance comparisons between models are sometimes inconsistent, and claims of superiority should be tempered. Calibration results indicating borderline or poor fit require discussion.

Explainability and XAI Claims: The manuscript emphasizes explainable AI, yet the practical contribution of ALE and LIME analyses to public health decision-making is not clearly articulated. The authors should clarify how these explanations add value beyond traditional regression outputs.

Data Availability and Reproducibility: The current Data Availability Statement does not meet PLOS requirements. Public deposition of de-identified data and analytic code is essential for transparency and reproducibility.

Interpretation and Scope of Conclusions: Several conclusions particularly regarding sex differences, behavioral readiness, and intervention design extend beyond what can be supported by this dataset. These interpretations should be reframed as exploratory and hypothesis-generating.

Manuscript Structure and Clarity: The manuscript would benefit from substantial condensation, especially in the Discussion, and clearer separation between results, interpretation, and implications.

This is a promising and relevant study with potential contribution to digital public health research. However, substantial revisions are required to strengthen methodological rigor, transparency, and alignment between data, analyses, and conclusions.

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Reviewer #1: No

Reviewer #2: Yes: Andrews BaidooAndrews Baidoo

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Attachments

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Submitted filename: 1.docx

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Submitted filename: reviews.docx

Identifying Factors Associated with Vaping Cessation in Young Adults: A Machine Learning and XAI Approach.

PDIG-D-25-00724R1

Dear Dr Mahajan,

We are pleased to inform you that your manuscript 'Identifying Factors Associated with Vaping Cessation in Young Adults: A Machine Learning and XAI Approach.' has been provisionally accepted for publication in PLOS Digital Health.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow-up email from a member of our team.

Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.

IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. Requests for major changes, or any which affect the scientific understanding of your work, will cause delays to the publication date of your manuscript.

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 digitalhealth@plos.org.

Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Digital Health.

Best regards,

Laura Sbaffi, PhD, MA, MSc

Section Editor

PLOS Digital Health

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Additional Editor Comments (if provided):

Reviewer Comments (if any, and for reference):