All reviews of published articles are made public. This includes manuscript files, peer review comments, author rebuttals and revised materials. Note: This was optional for articles submitted before 13 February 2023.
Peer reviewers are encouraged (but not required) to provide their names to the authors when submitting their peer review. If they agree to provide their name, then their personal profile page will reflect a public acknowledgment that they performed a review (even if the article is rejected). If the article is accepted, then reviewers who provided their name will be associated with the article itself.
Thank you for submitting the article to PeerJ Computer Science. All reviewers have no further suggestions and agree to accept the article for publication.
[# PeerJ Staff Note - this decision was reviewed and approved by Claudio Ardagna, a 'PeerJ Computer Science' Section Editor covering this Section #]
I appreciate the authors for revising the manuscript, and writing a clear and detailed response letter. The revision has addressed my previous comments.
No comments
No comments
No comments
No more comments
NA
NA
Thank you for submitting the manuscript to PeerJ Computer Science. Based on the comments from three reviewers, we would like to invite you to revise the manuscript. The major issues include the writing and the report of the parameters the authors used in the manuscript. More details about the methods might need to be addressed for the reproductive purpose. Please let us know if you have any questions in the meantime.
**PeerJ Staff Note:** Please ensure that all review and editorial comments are addressed in a response letter and that any edits or clarifications mentioned in the letter are also inserted into the revised manuscript where appropriate.
**Language Note:** The review process has identified that the English language must be improved. PeerJ can provide language editing services - please contact us at [email protected] for pricing (be sure to provide your manuscript number and title). Alternatively, you should make your own arrangements to improve the language quality and provide details in your response letter. – PeerJ Staff
1. The authors used clear and unambiguous language in writing the manuscript.
2. There are minor grammatical errors and awkward phrasings throughout the manuscript. A thorough proofreading or professional editing service could help improve the overall quality of writing.
3. The references section is comprehensive but should be updated to include more recent studies, particularly from the last two to three years
4. For section 6 Data Analysis, the steps for data processing are redundant and should either be omitted or moved to the Appendix.
5. In section 7 Results and Discussion line 268 to line 277, the authors described the tables in detail; this also seem redundant, and the information is already presented in the tables.
6. Figure 4, the pie chart, does not add much information and should be omitted.
1. The experimental design is appropriate, and the aims and scope aligns with those of the journal.
2. The research question is well-defined.
3. The authors briefly listed the five models used in the paper. However, the reason for choosing these models as well as a brief description of the models is lacking. I recommend the author add these to the paper.
4. In the Results section, it is recommended to discuss the statistical significance of the results. Include any hypothesis tests or confidence intervals that support the robustness of the findings; that authors could also discuss the importance of different features used in the models. Highlight which features had the most significant impact on model performance and why they are important for TB diagnosis.
1. If applicable, provide policy recommendations based on the study's findings. Suggest how health policies could be adapted to incorporate ML-based TB diagnosis tools and improve public health outcomes.
NA
Karmani & Chandio et al. proposed using machine learning (ML) approaches to predict early Tuberculosis (TB) diagnosis. Using ~1,200 self-administered survey distributed among three hospitals in Pakistan, the authors collected responses from patients and employed different machine learning approaches to train classifiers for TB diagnosis. This work is particularly important for TB prevention and diagnosis in developing countries such as Pakistan with high TB burden. The authors provided a comprehensive overview of TB disease and literature review on TB classifiers. Overall, I recommend the authors provide more details in describing features included in the model and model training.
1. Can the authors provide brief description on features included in the survey/model?
2. Can the authors provide more details in describing model training such as hyperparameter tuning?
3. From line 197-199, please provide numerical values or create tables to show percentage of missingness.
4. For figure 5, it would be more informative to plot proportions instead of raw counts for categorical features.
1. If possible, can the authors add feature importance to interpret which feature(s) drive this high accuracy of prediction? This will be very informative for epidemiologist to understand the risk factor for TB and thus better manage TB in population.
2. Since different ML models gave different performances on four metrics, can the authors provide insights on which metric is more important in TB early diagnosis for the purpose of model selection? For example, early diagnosis is very important for TB so that high sensitivity is favored over precision.
NA
NA
NA
See attached PDF
All text and materials provided via this peer-review history page are made available under a Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.