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.