PONE-D-25-01298Integrated Analysis and Prediction of Global Happiness Index by Combining Integrated Multilayer Clustering and Machine LearningPLOS ONE

Dear Dr. Yang,

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PLOS ONE

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(3) Ensure the work reproducibility by showing the code or the detailed algorithm starting from data preprocessing up to results.

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Comments to the Author

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

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Reviewer #1: Partly

Reviewer #2: No

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

Reviewer #1: Partly

Reviewer #2: No

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: No

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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: 1. Vague Title & Abstract:

a. Problem: The title and abstract use very general terms like "combining" and "integrated" without making it apparent what the research's unique contribution is.

b. Recommendation: One suggestion would be to make the title more precise, such as "Predicting Global Happiness Using Multilayer Clustering and Classification Models." The research issue and technique, including "exploring the underlying patterns" and "integrating clustering and prediction models," should be clearly stated in the abstract.

2. Inconsistent Citation Style:

a. Problem: Throughout the work, different citation styles are used.

b. Recommendation: Use a consistent style (such as IEEE or APA) consistently. Use, for instance, "Chakraborty and Tsokos (2021)," "Rendón et al. (2011)," "the NbClust tool developed by Charrad et al. (2014)," or even "[Author et al., Year]" .

3. Non-Standard Sectioning:

a. Problem: Section 1 ("Introduction") is not formatted like a typical academic work.

b. Recommendation: Add a distinct "Literature Review" section (for example, Section 2). Provide a clearer and more succinct structure to the remaining sections (e.g., avoid numerous sub-sections).

4. Inadequate Related Work:

a. Problem: The "Related Work" section as it currently exists is too short and does not adequately relate to the goals of the study.

b. Recommendation: Extend the section on related work to offer a thorough analysis of pertinent studies on the following topics: Multilayer Clustering techniques; Machine Learning models for happiness prediction; Integration of clustering and prediction models; Global Happiness Index. Describe how the current study adds to and deviates from earlier research in the topic

5. Unclear Methodology Flow:

a. Problem: The methodology in Section 2 does not follow a logical or obvious flow.

b. Recommendation: To enhance readability and general flow, combine relevant subsections. The study method, including data collection, preprocessing, feature selection, clustering, model development, and evaluation, should be described in detail, step-by-step.

6. Disconnected Statistical Analysis:.

a. Problem: It seems that the statistical analysis in Section 3 is not connected to the approach that is being given.

b. Recommendation: Make sure the findings of the statistical analysis directly support the approach that was selected. Clearly describe how the data was analyzed and the research topics addressed using statistical approaches.

7. Intertwined Discussion & Results:

a. Problem: Sections 4 (Discussion) and 3 (Results) are related and may be more cohesively combined.

b. Recommendation: Combine pertinent portions of these sections to produce a more logical and perceptive presentation of the results. Present the findings succinctly and clearly, then go into great depth about their limits, implications, and possible future study avenues.

8. Unclear Degrees of Freedom

a. Problem: It's unclear why Table 4's degrees of freedom are set to 2.

b. Recommendation: Clearly state the reasoning behind this decision, including how many variables were employed in the analysis.

Give a more thorough explanation of the parameters and statistical approaches.

9. The Integrated Model Is Not Clear:

a. Problem: The integration mechanism between the prediction and clustering models is not adequately explained in the text

b. Recommendation: Give a thorough explanation of how the construction and assessment of the prediction models are influenced by or guided by the clustering results. Showcase the special benefits of this combined strategy above just employing prediction or clustering methods.

10. General lucidity:

a. Problem: The technique and outcomes are not presented in a clear and succinct manner overall.

b. Recommendation: One recommendation is to revise the entire document to enhance the presentation's clarity and flow. Whenever possible, steer clear of jargon and speak in plain, simple terms. Make sure the paper has a clear structure and is straightforward for readers to follow.

Reviewer #2: Please find below my remarks:

1) The manuscript utilizes kmeans clustering and supervised machine learning algorithms such as random forest and XGBoost to analyze and predict happiness score - however these are widely used techniques. The study does not introduce any novel methodology or new theoretical framework.

2) The paper does not adequately discuss explainability techniques to interpret the model’s decisions. While machine learning prediction is there, but a lack of explainability does not allow us to understand how the model is making the decision.

3) The selection of three clusters seems arbitrary. I understand that the optimal number of clusters is derived from the elbow method and average silhouette score, but the authors did not provide why three is an optimal number.

4) The study considers economic and social factors but does not incorporate psychological, environmental, or cultural variables, which are crucial in happiness studies.

5) Authors could have done ablation studies to understand the impact of various factors on happiness score.

6) The model is trained and tested on the same dataset (World Happiness Report). Cross-validation with other happiness or well-being datasets would have enhanced the generalizability of the findings.

7) Policy implications should ideally differ across countries. However, the discussion on policy implications is very generic and does not provide concrete recommendations based on different country profiles.

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Reviewer #1: Yes

Reviewer #2: No

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PONE-D-25-01298R1Analyzing and Predicting Global Happiness Index via Integrated Multilayer Clustering and Machine Learning ModelsPLOS ONE

Dear Dr. Yang,

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.

1. The research framework should be smaller, and please watch out for arrows that don't align well in the left part of the figure. It would be nice if the figure had a sequence of steps.
2. All the figures look blurry. Please insert them in the final version to see the full picture (pending acceptance).
3. While the reviewers and the academic editor mention issues related to missing sections, the authors fail to address these issues fairly. The author should spare separate sections for discussion, limitations or threats to validity, and impact of the study (optional). The research objectives addressed in section 1.3 are expected to be discussed. The study's limitations, mainly found in many machine learning papers, should be described with their respective mitigation approaches.   

Please submit your revised manuscript by Apr 17 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.

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,

Issa Atoum

Academic Editor

PLOS ONE

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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.

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Analyzing and Predicting Global Happiness Index via Integrated Multilayer Clustering and Machine Learning Models

PONE-D-25-01298R2

Dear Dr. Yang,

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.

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Kind regards,

Issa Atoum

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments: