Next Article in Journal
Graph Sampling-Based Multi-Stream Enhancement Network for Visible-Infrared Person Re-Identification
Next Article in Special Issue
Advancing Breast Cancer Diagnosis through Breast Mass Images, Machine Learning, and Regression Models
Previous Article in Journal
Accurate Visual Simultaneous Localization and Mapping (SLAM) against Around View Monitor (AVM) Distortion Error Using Weighted Generalized Iterative Closest Point (GICP)
 
 
Communication
Peer-Review Record

Explainable Risk Prediction of Post-Stroke Adverse Mental Outcomes Using Machine Learning Techniques in a Population of 1780 Patients

Sensors 2023, 23(18), 7946; https://doi.org/10.3390/s23187946
by Chien Wei Oei 1,2, Eddie Yin Kwee Ng 2,*, Matthew Hok Shan Ng 3, Ru-San Tan 4,5, Yam Meng Chan 6, Lai Gwen Chan 7,8 and Udyavara Rajendra Acharya 9
Reviewer 1:
Reviewer 2:
Sensors 2023, 23(18), 7946; https://doi.org/10.3390/s23187946
Submission received: 15 August 2023 / Revised: 13 September 2023 / Accepted: 15 September 2023 / Published: 17 September 2023
(This article belongs to the Special Issue AI-Based Automated Recognition and Detection in Healthcare)

Round 1

Reviewer 1 Report

The review file is attached. 

Comments for author File: Comments.pdf

Author Response

Dear Reviewer, 

Thankful for taking your time to review my manuscript and provide the relevant comments for improvement.

I have uploaded my responses to your comments in the file attached.

Thank you!

Warmest Regards,
Chien Wei

Author Response File: Author Response.pdf

Reviewer 2 Report

I believe the paper's topic is relevant, but the introduction  should  be enhanced. A literature review should be included in the introduction or as a distinct section.  Indeed, 69 papers were cited in the "References" section, and their relevance to the study should be further clarified, which helps to highlight the relevance and additional value of the proposed research. 

The presentation of the  results require further improvement and some details should be added. As example, the authors did not mention the number of features considered for the  results shown in table 2. Is the number of features same for all ML algorithms? if yes, how many features are  considered? So the experimental conditions that helped to obtained the results reported in table 2, should very detailed and justified. 

The cross validation results should be presented.

It is not clear how the results in figure 3 relate to those in table 2 or how they are discussed. Otherwise, it appears that the study is unrelated to these outcomes. 

The SHAP values for several features are displayed in Figure 4. But why were only ten features taken into account, I wonder? How low a SHAP value must the feature be before it loses all meaning? Figure 4's results appeared to be inconsistent with the tests conducted to choose the ML method.  

I believe that the comparison with state of the art should be examined in greater depth in the discussion subsection. 

The overall research design needs to be enhanced.  In particular,   sections on introduction and results need to be improved. 

Author Response

Dear Reviewer, 

Thanks for spending time to review the manuscript and to provide your insightful feedbacks.

Apologies for the slight delay as I was national service duty and have limited access to internet.

I have provided my responses as attached. Thank you!

Warmest Regards,
Chien Wei

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

The new version is improved compared to the previous one. 

 

Back to TopTop