Title: Machine Learning Survival Models for Non-alcoholic Fatty Liver Disease Based on a
Health Checkup Cohort
Version: 0 Date: 27 May 2025
Reviewer:  Monica Mischitelli
Reviewer's report:
Authors explore the possibility to use a random survival forests (RSF) model to better investigate
the distribution of NAFLD risk and time.
The study is really interesting, nevertheless, I think revisions are required.
First: it needs to better describe study population, how were subjects who followed diets
considered? For example, were celiac subjects or those with particular food allergies excluded?
Were pregnant women excluded from the study? How many grams of alcohol does medium/
heavy consumption correspond to? What is the questionnaire administered based on? How were
glucose intolerance and insuline resistance treated?
I also believe it is absolutely necessary to specify why some blood count parameters were chosen.
I understand the value of platelets that, in subjects affected by fatty liver disease, contribute
to the creation of a persistent low grade inflammatory state, on the other hand I believe that
it is absolutely necessary to clarify the choice of the use of red blood cells, hemoglobin and
the remaining parameters of the blood count, especially white blood cells included in the top
5 variables in the RSF.
I also think it is quite obvious that BMI and lipid profile work as good predictors, I would instead
like to better clarify the role of liver function indices and why renal function indices were also
investigated.
Finally, it would be useful to specify the type of parameter that allowed to evaluate the enrolled
subjects as NAFLD-free or affected by NAFLD after 3 years of follow-up.
In conclusion, given the numerous and highlighted limitations of the study, I believe it is
necessary to better describe the study cohort and argue more precisely the choice and utility of
the predictors included in the population stratification model.
Title: Machine Learning Survival Models for Non-alcoholic Fatty Liver Disease Based on a
Health Checkup Cohort
Version: 0 Date: 27 May 2025
Reviewer:  Monica Mischitelli
Reviewer's report:
Authors explore the possibility to use a random survival forests (RSF) model to better investigate
the distribution of NAFLD risk and time.
The study is really interesting, nevertheless, I think revisions are required.
First: it needs to better describe study population, how were subjects who followed diets
considered? For example, were celiac subjects or those with particular food allergies excluded?
Were pregnant women excluded from the study? How many grams of alcohol does medium/
heavy consumption correspond to? What is the questionnaire administered based on? How were
glucose intolerance and insuline resistance treated?
I also believe it is absolutely necessary to specify why some blood count parameters were chosen.
I understand the value of platelets that, in subjects affected by fatty liver disease, contribute
to the creation of a persistent low grade inflammatory state, on the other hand I believe that
it is absolutely necessary to clarify the choice of the use of red blood cells, hemoglobin and
the remaining parameters of the blood count, especially white blood cells included in the top
5 variables in the RSF.
I also think it is quite obvious that BMI and lipid profile work as good predictors, I would instead
like to better clarify the role of liver function indices and why renal function indices were also
investigated.
Finally, it would be useful to specify the type of parameter that allowed to evaluate the enrolled
subjects as NAFLD-free or affected by NAFLD after 3 years of follow-up.
In conclusion, given the numerous and highlighted limitations of the study, I believe it is
necessary to better describe the study cohort and argue more precisely the choice and utility of
the predictors included in the population stratification model.
Please review this paper.
user
Title: Machine Learning Survival Models for Non-alcoholic Fatty Liver Disease Based on a
Health Checkup Cohort
Version: 0 Date: 27 May 2025
Reviewer:  Monica Mischitelli
Reviewer's report:
Authors explore the possibility to use a random survival forests (RSF) model to better investigate
the distribution of NAFLD risk and time.
The study is really interesting, nevertheless, I think revisions are required.
First: it needs to better describe study population, how were subjects who followed diets
considered? For example, were celiac subjects or those with particular food allergies excluded?
Were pregnant women excluded from the study? How many grams of alcohol does medium/
heavy consumption correspond to? What is the questionnaire administered based on? How were
glucose intolerance and insuline resistance treated?
I also believe it is absolutely necessary to specify why some blood count parameters were chosen.
I understand the value of platelets that, in subjects affected by fatty liver disease, contribute
to the creation of a persistent low grade inflammatory state, on the other hand I believe that
it is absolutely necessary to clarify the choice of the use of red blood cells, hemoglobin and
the remaining parameters of the blood count, especially white blood cells included in the top
5 variables in the RSF.
I also think it is quite obvious that BMI and lipid profile work as good predictors, I would instead
like to better clarify the role of liver function indices and why renal function indices were also
investigated.
Finally, it would be useful to specify the type of parameter that allowed to evaluate the enrolled
subjects as NAFLD-free or affected by NAFLD after 3 years of follow-up.
In conclusion, given the numerous and highlighted limitations of the study, I believe it is
necessary to better describe the study cohort and argue more precisely the choice and utility of
the predictors included in the population stratification model.
assistant