PONE-D-21-28367Body fat prediction through feature extraction based on anthropometric and laboratory measurementsPLOS ONE

Dear Dr. Chiong,

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.

In particular:

• The "methods" section is unclear and detailed setup of each model used during experiments is not presented,
• There is no deep analysis of feature extraction results,
• Presentation needs improvements:
  • Fig. 7 and Fig. 9 are hard to read - please consider using log scale for vertical axis,
  • Table 7 - please remove not needed zeroes from values,
• Selection of simple models is not discussed and justified. Models performing dynamic (contextual) data processing could give better results even for simple NNs in comparison with solutions with static FA/PCA/ICA (please see e.g. Sigma-if neural network),
• Conclusions are very shallow.

Please submit your revised manuscript by Nov 22 2021 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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Maciej Huk, Ph.D.

Academic Editor

PLOS ONE

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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?

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: No

Reviewer #2: Yes

Reviewer #3: Yes

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

Reviewer #1: No

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: In this paper, authors tried three standard feature extraction methods on two open datasets. Verified with three vaninal models.

The reason I reject this paper is that: this is a "toy paper". It will be a shame for this journal and machine learning community to publish something like this in 2021. I may grade a 'B' if this was a ML course project report from a junior university student.

- Motivation

What's the motivation of this paper? In the conclusion, the author states

"Experimental results showed that feature extraction methods can reduce features without incurring significant loss of information for body fat prediction."

"In Case 1, with only six extracted features, the prediction models exhibited better performance than the models without using feature extraction."

"For Case 2, PCA was the most effective in improving model performance. "

Do we need this paper to tell us this? Can't we find any similar sentences in any basic ML books for university students?

"Although the MLP with PCA had the best prediction accuracy, it required significantly more computation time. This means XGBoost is more appropriate for real-world applications, given its similar prediction accuracy and greater efficiency."

Didn't the author know that in real-world, complex neural networks have been used everywhere? This statement is so shabby and out of date.

- Experiment setup

Author didn't give detailed setup of each model.

"Case 1 contained 252 samples with 13 input features"

"After pre-processing, 862 samples with 39 features were obtained."

Is this a joke? With this amount of data and feature dimensions, authors are talking about "curse of dimension". Are we living in 1980s?

No deep analysis of feature extraction results.

- others

section 2.2 is waste of paper. You can find all those in wikipedia or any ML tutorial books/blogs/etc.

Reviewer #2: In this manuscript, the authors investigate the effectiveness of feature extraction for body fat prediction. Their results on two real body fat datasets demonstrate that the prediction models perform better on incorporating feature extraction for body fat prediction. The paper is well organized; however, I have some concerns:

1. The method section is poorly written and rather unclear on many points; You don't need to show every step/equation for well-known methods. Just summarize them and describe your settings (e.g., # of node, activation function, LR...)

2. The task is a continuous prediction (body fat percentage), and how did you perform SVM/RF on continuous prediction tasks? Did you group the body fat percentage into different classes?

3. Can authors compare them with methods without feature extraction (just min-max normalization on raw features)? This is an important baseline.

4. On page1 last row, what is "Lagrange multiplier measurements (features)"? Typo?

Reviewer #3: This work studies the problem of predicting body fat using a feature extraction method followed by a learning algorithm. For feature extraction, they tried ICA, PCA, and FA (factor analysis), and for learning and prediction, they used MLP, SVM, RF (random forest), and XGboost. According to the experimental validation work, XGBoost with FA has the best approximation ability. Overall the paper is easy to read and understand. Here are some issues I would like to see addressed:

1. State the novelty of the work.

2. Fix some notational inconsistencies, e.g., in section 2.2.1, stick with either e_i or error_i

3. Explain why you think ICA, PCA, and FA are the most useful feature extraction one should use and why MLP, SVM, RF, and XGBoost are picked as the prediction methods.

4. Why is this work confined only to a body fat prediction? Can it be used for any other prediction problem with only a large number of real-valued features?

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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.

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

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Body fat prediction through feature extraction based on anthropometric and laboratory measurements

PONE-D-21-28367R1

Dear Dr. Chiong,

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 for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, 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,

Maciej Huk, Ph.D.

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 #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 #2: Yes

Reviewer #3: Yes

**********

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

Reviewer #2: (No Response)

Reviewer #3: 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 #2: Yes

Reviewer #3: 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 #2: Yes

Reviewer #3: 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 #2: (No Response)

Reviewer #3: All of my comments have been adequately addressed. Thank you. The revised manuscript looks much better now.

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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 #2: No

Reviewer #3: No