Enhancing Decision-Making and Data Management in Healthcare: A Hybrid Ensemble Learning and Blockchain Approach

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

1. The first section of the introduction does a great job of describing why big data is important in healthcare but fails to provide as good a background for integrating ensemble learning and blockchain in addressing particular problems. A more in-depth discussion of the novel aspects of this approach in contrast to other techniques is requested.

2. The manuscript is structured as a systematic review instead of an original research article and contains much of its current content. There should be a clear distinction between the new contributions of this paper and a comprehensive discussion of existing methods. Rather, repositioning it explicitly as a systematic review would have strengthened its strength.

3. The methods section provides a good framework but needs further elaboration on:

-) Data sampling and preprocessing process.

-) Details of practical implementation of ensemble learning and integration with blockchain.

-)The adaptability of the proposed method to other datasets.

4. While the results are promising, they could be improved by:

-) The authors also make more detailed comparisons with state-of-the-art methods.

-) Validation of performance differences by adding statistical tests.

-) Adding additional visualizations (confusion matrices, time-based performance trends etc.)

5. The conclusion is well articulated, yet could be made more concrete by being informed by specific limitations and proposed concrete future work directions, e.g. extensibility to other domains or leveraging more advanced neural network techniques.

6. The manuscript should be edited by a professional as there are many typical problems with the language and the text's coherence. There are cases where a sentence is long and compounds and this puts off the flow of technical details.

7. This study should be revised as a review paper, one has given a lot of discussion of related work and little breakthrough empirical contribution. The authors will be able to synthesize previous research and place their proposed framework in the context of the future direction of the field.

Comments on the Quality of English Language

-) Some of the sentences are very long, or too complex, making the technical content hard to comprehend. These are well broken into short, more direct sentences and clarity would be improved upon.

-) The manuscript contains minor grammatical issues and awkward phrasing. One example would be that technical terms could be used more consistently.

-) Sections and their ideas could make smooth transitions between them, but shifting abruptly, occasionally disrupts the logical flow.

 

 

Author Response

C1: The first section of the introduction does a great job of describing why big data is important in healthcare but fails to provide as good a background for integrating ensemble learning and blockchain in addressing particular problems. A more in-depth discussion of the novel aspects of this approach in contrast to other techniques is requested.

R1: The authors are thankful for providing their valuable time and efforts in order to improve the quality of the manuscript. The updated manuscript now has the proper integration of ensemble learning and blockchain in the 1.1. Motivation and Objective section.

C2: The manuscript is structured as a systematic review instead of an original research article and contains much of its current content. There should be a clear distinction between the new contributions of this paper and a comprehensive discussion of existing methods. Rather, repositioning it explicitly as a systematic review would have strengthened its strength.

R2: The authors are thankful for anonymous reviewers. The paper is further revised in order to highlight the contribution of the paper in 1.2. Contribution section:

• Integrating the learning method with secure mechanism in order to detect the malicious behaviour of the communicating devices along with improving the accuracy of the proposed mechanism.
• The boosting ensemble learning mechanism is used to validate the data sampling while recording and generating the information from intelligent devices in order to provide accurate decision-making.
• Blockchain mechanism is used for identifying the malicious activities and continuous surveillance of heterogeneous information recorded by several intelligent devices while processing and communicating the information in the network.

C3. The methods section provides a good framework but needs further elaboration on: -) Data sampling and preprocessing process;-) Details of practical implementation of ensemble learning and integration with blockchain; -) The adaptability of the proposed method to other datasets.

R3: The proposed mechanism is further improved in the updated version of the manuscript. The details are provided as below:

• Data sampling and preprocessing information is provided in 3.1.1. Data Processing section
• Details of practical implementation of ensemble learning and integration with blockchain are presented in 4.1. Methodology section.
• The details of adaptability of the proposed method to other datasets in provided in 4. Performance Analysis section.

C 4. While the results are promising, they could be improved by:-) The authors also make more detailed comparisons with state-of-the-art methods. -) Validation of performance differences by adding statistical tests. -) Adding additional visualizations (confusion matrices, time-based performance trends etc.)

R4: The performance section is further improved in order to improve the overall quality of the paper. The details are provided in 4.2.1. Results Analysis.

C5. The conclusion is well articulated, yet could be made more concrete by being informed by specific limitations and proposed concrete future work directions, e.g. extensibility to other domains or leveraging more advanced neural network techniques.

R5: The conclusion section is further improved in the updated version of the paper.

C6. The manuscript should be edited by a professional as there are many typical problems with the language and the text's coherence. There are cases where a sentence is long and compounds and this puts off the flow of technical details.

R6: The authors are apologized for this error. The entire manuscript is further improved by removing grammatical mistakes.

C7. This study should be revised as a review paper, one has given a lot of discussion of related work and little breakthrough empirical contribution. The authors will be able to synthesize previous research and place their proposed framework in the context of the future direction of the field.

R7: The present paper is further improved by detailing the research gap, contribution and practical implementation of proposed scenario and validate it against existing approach.

Comments on the Quality of English Language

C8: Some of the sentences are very long, or too complex, making the technical content hard to comprehend. These are well broken into short, more direct sentences and clarity would be improved upon.

R8: The authors apologised for this error. The manuscript is further improved by correcting the sentences and English.

C9: The manuscript contains minor grammatical issues and awkward phrasing. One example would be that technical terms could be used more consistently.

R9: The grammatical issues are further resolved from the paper.

C10: Sections and their ideas could make smooth transitions between them, but shifting abruptly, occasionally disrupts the logical flow.

R10: The authors apologized for this error. The flow of entire manuscript is further revised in the updated version.

Reviewer 2 Report

Comments and Suggestions for Authors

Thank you for submitting your manuscript titled "Enhancing Decision-Making and Data Management in Healthcare: A Hybrid Ensemble Learning and Blockchain Approach" for consideration in the special issue of Healthcare. We appreciate your effort in addressing a critical topic within healthcare analytics and data management, which aligns with the theme of leveraging statistical methodologies to improve healthcare outcomes.

Below, I have provided a set of constructive comments and observations aimed at enhancing the quality and clarity of your manuscript. These suggestions are intended to ensure that your work achieves its full potential and aligns closely with the objectives of the special issue.

Based on the manuscript's content, the study appears to hold substantial potential for contributing to healthcare analytics and decision-making. While some methodological aspects require further elaboration, the manuscript is a good candidate for inclusion in the special issue.

Innovation and Relevance:

The manuscript proposes a hybrid mechanism that combines ensemble learning techniques with blockchain technology, focusing on decision-making and data management in healthcare. This interdisciplinary approach is innovative and represents a valuable application of advanced technologies to address critical challenges in healthcare data processing.

The integration of blockchain for secure data storage and real-time surveillance complements the ensemble learning approach by ensuring data reliability, an essential factor in healthcare analytics.

Clarity and Structure:

The manuscript is well-organized, providing a clear flow of ideas from problem identification to methodology and results. The introduction establishes the significance of the problem, while the results section provides a comparative analysis of the proposed method.

However, the explanation of the statistical validation techniques employed could be more detailed to enhance transparency and reproducibility.

Technical Strengths:

The authors utilize quantitative experiments to demonstrate the superiority of their approach compared to existing methods. These comparisons, supported by metrics such as accuracy and efficiency, provide initial evidence of the method's effectiveness.

Potential Limitations:

While the manuscript showcases promising results, the scalability of the proposed blockchain-based system and its applicability in diverse healthcare scenarios remain underexplored.

Ethical and privacy concerns related to implementing blockchain in healthcare, though mentioned, could benefit from deeper discussion.

Technical aspects:

Formatting issue in the mathematical formulas, labeled (1) through (10). Words within the formulas appear without proper spacing or separation (e.g., by underscores or other delimiters). This affects the readability and clarity of the mathematical expressions.

Author Response

Thank you for submitting your manuscript titled "Enhancing Decision-Making and Data Management in Healthcare: A Hybrid Ensemble Learning and Blockchain Approach" for consideration in the special issue of Healthcare. We appreciate your effort in addressing a critical topic within healthcare analytics and data management, which aligns with the theme of leveraging statistical methodologies to improve healthcare outcomes. Below, I have provided a set of constructive comments and observations aimed at enhancing the quality and clarity of your manuscript. These suggestions are intended to ensure that your work achieves its full potential and aligns closely with the objectives of the special issue.

C1: Based on the manuscript's content, the study appears to hold substantial potential for contributing to healthcare analytics and decision-making. While some methodological aspects require further elaboration, the manuscript is a good candidate for inclusion in the special issue.

R1: The authors are highly thankful of anonymous reviewer for improving the overall quality of the paper. The manuscript is further revised by detailing the contribution and research gap that can be fitted in the present SI of the journal.

Innovation and Relevance:

C2: The manuscript proposes a hybrid mechanism that combines ensemble learning techniques with blockchain technology, focusing on decision-making and data management in healthcare. This interdisciplinary approach is innovative and represents a valuable application of advanced technologies to address critical challenges in healthcare data processing. The integration of blockchain for secure data storage and real-time surveillance complements the ensemble learning approach by ensuring data reliability, an essential factor in healthcare analytics.

R2: The authors are thankful for providing their valuable time and efforts for improving the overall quality of the paper.  

Clarity and Structure:

C3: The manuscript is well-organized, providing a clear flow of ideas from problem identification to methodology and results. The introduction establishes the significance of the problem, while the results section provides a comparative analysis of the proposed method. However, the explanation of the statistical validation techniques employed could be more detailed to enhance transparency and reproducibility.

R3: The validation section is further enhanced by detailing the out-performance of proposed mechanism in comparison of existing approach. The details are provided in 4.2.1. Result Analysis section.

Technical Strengths:

C4: The authors utilize quantitative experiments to demonstrate the superiority of their approach compared to existing methods. These comparisons, supported by metrics such as accuracy and efficiency, provide initial evidence of the method's effectiveness. While the manuscript showcases promising results, the scalability of the proposed blockchain-based system and its applicability in diverse healthcare scenarios remain underexplored.

R4: The scalability and the inclusion of self-learning algorithms to understand the misbehaving pattern of the network can be further included in future directions of the paper.

C5: Ethical and privacy concerns related to implementing blockchain in healthcare, though mentioned, could benefit from deeper discussion.

R5:  The privacy concerns are now discussed in 4.2.1. Results Analysis section.

Technical aspects:

C6: Formatting issue in the mathematical formulas, labelled (1) through (10). Words within the formulas appear without proper spacing or separation (e.g., by underscores or other delimiters). This affects the readability and clarity of the mathematical expressions.

R6: The authors are thankful for anonymous reviewers for improving the overall quality of the paper. The formatting of the mathematical formulas is further corrected in the updated version.

 

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

All remarks have been taken into consideration by the authors.

However, I would recommend the dimensions of figures 7-11.

Comments on the Quality of English Language

OK.