Hybrid Model Feature Selection with the Bee Swarm Optimization Method and Q-Learning on the Diagnosis of Coronary Heart Disease
Round 1
Reviewer 1 Report
In this work, the authors proposed a Hybrid Model using Bee Swarm Optimization Method and Q-Learning for predicting the Coronary Heart Disease. This model is tested on standard datasets available in the literature. The results are compared with the models. There are some issues which authors need to incorporate in their manuscript.
1. Some of the abbreviations are missing and some are repeated.
2. Some of the sentences have grammatical errors- For example in " The datasets that were used were Z-Alizadeh Sani, Statlog, Cleveland, and Hungary", were is used two times.
3. Compare your results with the latest techniques. Ref [23] and [24] are 4-5 years old.
4. Kindly tabulate the features selected, it will increase the interest of the reader.
Author Response
Please see the attachment.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
1. The dataset used during the study is very common. There are many studies available on that. I would suggest the author should try some other dataset in their study as well to see if their approach supports other datasets as well.
2. Only two study was compared. Why? Please provide more explanations.
3. Figure 1 is much more complex than the explanations. Please explain why your approach requires such extensive steps to improve the model's performance.
4. Please explain how your method is novel compared to existing approaches.
5. Please explain the limitations.
6. Please explain other alternative approaches.
7. Please explain if your dataset is imbalanced what will be the scenario of reducing the feature using your proposed approaches?
Author Response
Please see the attachment
Author Response File: Author Response.pdf
Round 2
Reviewer 1 Report
Now the paper has been improved. It can be accepted in current form.
Reviewer 2 Report
The author addressed most of my comments. I would recommend this paper for publication.
