Title: Decoding IBS: A Machine Learning Approach to Psychological Distress and Gut-Brain
Interaction
Version: 0 Date: 26 Jun 2024
Reviewer:  Hiroshi Mihara
Reviewer's report:
Questions and Comments:
On the use of K-means clustering with k=3:
You have set the number of clusters to 3 for the K-means algorithm. Have you considered
including the results for k=2 and k=4 in your discussion to assess whether 3 is indeed the optimal
number of clusters?
On the specificity of the disease classification:
It is noted that your model accurately predicted the membership to the IBS group in 80% of the
cases in an unknown dataset. However, this accuracy was achieved in distinguishing between
only IBS and healthy controls. If the dataset were to include other disease groups, such as
patients with Functional Dyspepsia (FD), it is expected that the accuracy of discrimination would
decrease. Therefore, it is essential to clarify that the capability demonstrated is specific to the
binary distinction between these two groups.
Discussion of DGBI:
DGBI is mentioned in the abstract and introduction but not discussed. You indicate that the
sample is divided into three clusters, with only two and three being associated with DGBI. Is
it your understanding that cluster one includes a group not related to DGBI? Please elaborate
on your thoughts regarding this.
Clarification of abbreviations:
What does ME stand for?
IBS subtypes within the clusters:
Due to the small sample size, you have not factored IBS subtypes into the analysis. However,
what were the proportions of IBS-C, IBS-D, and IBS-M within the three identified clusters?
Recommendation for introduction:
It would be beneficial to summarize the overall findings and recent reports on IBS and the use
of machine learning and AI in the introduction. Discuss how AI has been applied to determine
treatment approaches and diagnose IBS with improved efficacy through colonic endoscopy
image analysis. Have there been any reports on clustering IBS patients using machine learning?
If so, how does your work differ?
Citation of relevant literature:
Given that there are publications on diagnosing and classifying IBS using machine learning, it
would be appropriate to reference how your findings differ from these existing studies.
Noman Haleem et al., Open Res Eur, 2023 Jan 27:3:19.
Elahe Mousavi et al., Sci Rep, 2023 Oct 28;13(1):18483.
Kazuhisa Tabata et al., Published: February 17, 2023, https://doi.org/10.1371/
journal.pdig.0000058.
Tarkan Karakan et al. Gut Microbes. 2022 Jan-Dec;14(1):2138672.
Title: Decoding IBS: A Machine Learning Approach to Psychological Distress and Gut-Brain
Interaction
Version: 0 Date: 26 Jun 2024
Reviewer:  Hiroshi Mihara
Reviewer's report:
Questions and Comments:
On the use of K-means clustering with k=3:
You have set the number of clusters to 3 for the K-means algorithm. Have you considered
including the results for k=2 and k=4 in your discussion to assess whether 3 is indeed the optimal
number of clusters?
On the specificity of the disease classification:
It is noted that your model accurately predicted the membership to the IBS group in 80% of the
cases in an unknown dataset. However, this accuracy was achieved in distinguishing between
only IBS and healthy controls. If the dataset were to include other disease groups, such as
patients with Functional Dyspepsia (FD), it is expected that the accuracy of discrimination would
decrease. Therefore, it is essential to clarify that the capability demonstrated is specific to the
binary distinction between these two groups.
Discussion of DGBI:
DGBI is mentioned in the abstract and introduction but not discussed. You indicate that the
sample is divided into three clusters, with only two and three being associated with DGBI. Is
it your understanding that cluster one includes a group not related to DGBI? Please elaborate
on your thoughts regarding this.
Clarification of abbreviations:
What does ME stand for?
IBS subtypes within the clusters:
Due to the small sample size, you have not factored IBS subtypes into the analysis. However,
what were the proportions of IBS-C, IBS-D, and IBS-M within the three identified clusters?
Recommendation for introduction:
It would be beneficial to summarize the overall findings and recent reports on IBS and the use
of machine learning and AI in the introduction. Discuss how AI has been applied to determine
treatment approaches and diagnose IBS with improved efficacy through colonic endoscopy
image analysis. Have there been any reports on clustering IBS patients using machine learning?
If so, how does your work differ?
Citation of relevant literature:
Given that there are publications on diagnosing and classifying IBS using machine learning, it
would be appropriate to reference how your findings differ from these existing studies.
Noman Haleem et al., Open Res Eur, 2023 Jan 27:3:19.
Elahe Mousavi et al., Sci Rep, 2023 Oct 28;13(1):18483.
Kazuhisa Tabata et al., Published: February 17, 2023, https://doi.org/10.1371/
journal.pdig.0000058.
Tarkan Karakan et al. Gut Microbes. 2022 Jan-Dec;14(1):2138672.
Please review this paper.
user
Title: Decoding IBS: A Machine Learning Approach to Psychological Distress and Gut-Brain
Interaction
Version: 0 Date: 26 Jun 2024
Reviewer:  Hiroshi Mihara
Reviewer's report:
Questions and Comments:
On the use of K-means clustering with k=3:
You have set the number of clusters to 3 for the K-means algorithm. Have you considered
including the results for k=2 and k=4 in your discussion to assess whether 3 is indeed the optimal
number of clusters?
On the specificity of the disease classification:
It is noted that your model accurately predicted the membership to the IBS group in 80% of the
cases in an unknown dataset. However, this accuracy was achieved in distinguishing between
only IBS and healthy controls. If the dataset were to include other disease groups, such as
patients with Functional Dyspepsia (FD), it is expected that the accuracy of discrimination would
decrease. Therefore, it is essential to clarify that the capability demonstrated is specific to the
binary distinction between these two groups.
Discussion of DGBI:
DGBI is mentioned in the abstract and introduction but not discussed. You indicate that the
sample is divided into three clusters, with only two and three being associated with DGBI. Is
it your understanding that cluster one includes a group not related to DGBI? Please elaborate
on your thoughts regarding this.
Clarification of abbreviations:
What does ME stand for?
IBS subtypes within the clusters:
Due to the small sample size, you have not factored IBS subtypes into the analysis. However,
what were the proportions of IBS-C, IBS-D, and IBS-M within the three identified clusters?
Recommendation for introduction:
It would be beneficial to summarize the overall findings and recent reports on IBS and the use
of machine learning and AI in the introduction. Discuss how AI has been applied to determine
treatment approaches and diagnose IBS with improved efficacy through colonic endoscopy
image analysis. Have there been any reports on clustering IBS patients using machine learning?
If so, how does your work differ?
Citation of relevant literature:
Given that there are publications on diagnosing and classifying IBS using machine learning, it
would be appropriate to reference how your findings differ from these existing studies.
Noman Haleem et al., Open Res Eur, 2023 Jan 27:3:19.
Elahe Mousavi et al., Sci Rep, 2023 Oct 28;13(1):18483.
Kazuhisa Tabata et al., Published: February 17, 2023, https://doi.org/10.1371/
journal.pdig.0000058.
Tarkan Karakan et al. Gut Microbes. 2022 Jan-Dec;14(1):2138672.
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