Dear Professor Iratxe Puebla,
Attached please find our revised manuscript (Manuscript ID: 1465970416279191)
entitled “A Study of Health Effects of Long-Distance Ocean Voyages on Seamen
Using a Data Classification Approach” for consideration of publication in BMC
Medical Informatics and Decision Making. In this revised version, we have carefully
addressed all issues raised by the reviewers. I hope that you find this revised version
acceptable for publication. I would like to take this opportunity to thank the
anonymous reviewers for their insightful comments. While the reviewers comments
really helped us to improve the quality and presentation of our paper, I have to very
respectfully point out that the paper seems to out of the domain of expertise of
reviewer #2 since all the authors felt that questions raised by the reviewer seem to be
somewhat off marker.
The following is a point-by-point list of we have addressed each and every issue
raised by the reviewers.
Sincerely yours,
Yunmei Lu (Ms)
Reviewer: 1
Comment :
pag 12 line 7: the values of LDH have to be interchanged in this way: 185.87 U/L
to 158.15 U/L frome pre-sailing to post sailing
pag 12 line 14: the fructosamine value after sailing( 191.41) doesn't match with
the corresponing one in table 5 (181.41) so one of them is uncorrect.
Response:
Thanks. We have made the correction on all of these in the revised version.
Reviewer: 2
Comment 1:
a) the approach that using classification method to detect change is not well defined.
for example, if the blood feature A of 4 seamen is 0, 1, 30, 40, before sailing, and they
are 5, 10, 35, 45, after sailing. It is obvious that this A will increase 4 because of
sailing. However, using the classification method as described in the paper, it is not
possible to classify these into 2 groups of (0,1,30,40) and (5,10,35,40), due to their
overlapping. One possibility to address this is to show that the features are quite stable
before the sailing ( i mean the differences among subjects are small). Further, the two
classes (before sailing and after sailing) are not sufficient to detect the changes of
features. Personally, I think the t-test on the differences of before and after sailing is
more reasonable.
Response:
Actually we have to respectfully disagree with the comment since our SVM-based can
actually distinguish the two cases that the reviewers used. Please note that SVM does
not find the separating planes in the feature space instead all the data points are
mapped into a higher dimensional kernel space as we have done in the paper. We did
provide some additional explanation in our SVM description to emphasize this point
in our revised manuscript.
In addition, we did a t-test to compare with the results of SVM-RFE we used in this
paper as the reviewer suggested. Out t-test results indicate that all the features selected
by SVM-RFE are all statistically significant.
Comment 2:
b) I think the description of SVM is wrong, or vague at least. In the paper, the dual
presentation is for non-separable cases, while the primal presentation is for separable
case. I suggest the author derive the SVM algorithm first, and them make the math
clear.
Response:
We had added additional material to make the derivation more clear.
Comment 3:
c) The backward elimination is not standard. For details on how to do backward
elimination, see Kwokleung Chan, Te-Won Lee, Pamela A. Sample, Michael
Goldbaum, Robert N. Weinreb, and Terrence J. Sejnowski. "Comparison of machine
learning and traditional classifier in glaucoma diagnosis". IEEE Transactions on
Biomedical Engineering, Vol 49 (9), 963-974, Sep 2002, # Pamela A. Sample,
Michael H. Goldbaum, Kwokleung Chan, Catherine Boden, Te-Won Lee, Christiana
Vasile, Andreas G. Boehm, Terrence Sejnowski, ChrisA. Johnson and Robert N.
Weinreb, "Using Machine Learning Classifiers to Identify Glaucomatous Change
Earlier in Standard Visual Fields", Investigative Ophthalmology and Visual Science,
Vol 43 (8), 2660-2665, Aug. 2002. and Goldbaum, M. H., Sample, P. A., Chan, K.,
Williams, J., Lee, T.-W., Blumenthal, E., Girkin, C. A., Zangwill, L. M., Bowd, C.,
Sejnowski, T., Weinre b, R. N., "Comparing machine classifiers for diagnosing
glaucoma from standard automated perimetry", Investigative Ophthalmology and
Visual Science, Vol 43 (1), 162-169, Jan. 2002.
Response:
We guess that by backward elimination, the reviewer meant the RFE algorithm. If this
is the case, we have to very respectfully disagree as RFE is widely used in the
literature in conjunction with SVM-based applications.
Comment 4:
d) the results need statistical analysis. The author need to show the relation between
the features and sailing are statistically significant, and not due to noise or
randomness. Also, the suggestions for the seaman life are based on indirect analysis.
Maybe the validation is beyond the scope of this paper, but i feel the conclusion is
rather weak.
Response: Again, I have to very respectfully disagree with this comment as our
validation procedure and results provide the needed statistical support for our
presented results.
Comment 5:
e) the language is not clear and hard to follow sometimes.
We have looked through the whole manuscript, and made numerous corrections. The
correspondence author of the paper is a senior researcher with 200+ journal
publications and four books all published in English. He has carefully gone through
the manuscript multiple times and polished the manuscript.
Reviewer: 3
No comments.
Reviewer: 4
Comment 1:
Unfortunately, there is serious technical error in the analysis. The data are clearly
paired, which means the samples are not independent. Support vector machine also
requires data to be drawn independently.
Response:
Yes, we agree that the data are paired but it should not affect the performance of our
SVM-based classifier since SVM does not independence of data points, which is one
of the reasons for its wide applications.
Comment 2:
Similarly, the paired t-test should be used instead of the t-test based feature selection
(page 10). In my opinion, the analysis has to be done again completely.
Response:
We agree with the reviewer, and have redone the t-test using paired t-test in the
revised manuscript.
Comment 3:
Quality of written English: Needs some language corrections before being published.
Thanks for the suggestion. Our senior author has thoroughly gone through the
manuscript multiple times and polished the manuscript.