Title: Machine Learning-Based Survival Rate Prediction of Korean Hepatocellular Carcinoma
Patients using multi-center data
Version: 6 Date: 10 Jan 2022
Reviewer's report:
The authors propose machine learning approaches to predict the outcome of Hepatocellular
Carcinoma survival in Korean cohort of patients. The work is interesting and may gain many
interests. However, I have minor concerns:
- Naming this method as big data is inaccurate. Big data is a terminology that has been used for
3Vs type of data(Velocity, Volume, Variety). The authors may argue that 2,000 records is big,
but it actually not. Big data through the manuscript can be changed to be datasets, and I think
it must be removed from the title.
- The imbalanced data section may highlight more on the used approach.mentionening the ratio
is good, but more details about the algorithm is needed. The Authors may also highlight it more
in the Introduction and highlight some other imbalanced data handling algorithm such as:
https://ieeexplore.ieee.org/document/7508607
Title: Machine Learning-Based Survival Rate Prediction of Korean Hepatocellular Carcinoma
Patients using multi-center data
Version: 6 Date: 10 Jan 2022
Reviewer's report:
The authors propose machine learning approaches to predict the outcome of Hepatocellular
Carcinoma survival in Korean cohort of patients. The work is interesting and may gain many
interests. However, I have minor concerns:
- Naming this method as big data is inaccurate. Big data is a terminology that has been used for
3Vs type of data(Velocity, Volume, Variety). The authors may argue that 2,000 records is big,
but it actually not. Big data through the manuscript can be changed to be datasets, and I think
it must be removed from the title.
- The imbalanced data section may highlight more on the used approach.mentionening the ratio
is good, but more details about the algorithm is needed. The Authors may also highlight it more
in the Introduction and highlight some other imbalanced data handling algorithm such as:
https://ieeexplore.ieee.org/document/7508607
Please review this paper.
user
Title: Machine Learning-Based Survival Rate Prediction of Korean Hepatocellular Carcinoma
Patients using multi-center data
Version: 6 Date: 10 Jan 2022
Reviewer's report:
The authors propose machine learning approaches to predict the outcome of Hepatocellular
Carcinoma survival in Korean cohort of patients. The work is interesting and may gain many
interests. However, I have minor concerns:
- Naming this method as big data is inaccurate. Big data is a terminology that has been used for
3Vs type of data(Velocity, Volume, Variety). The authors may argue that 2,000 records is big,
but it actually not. Big data through the manuscript can be changed to be datasets, and I think
it must be removed from the title.
- The imbalanced data section may highlight more on the used approach.mentionening the ratio
is good, but more details about the algorithm is needed. The Authors may also highlight it more
in the Introduction and highlight some other imbalanced data handling algorithm such as:
https://ieeexplore.ieee.org/document/7508607
assistant