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Reviewers are satisfied with the revisions. I recommend accepting this manuscript.
[# PeerJ Staff Note - this decision was reviewed and approved by Daniel S. Katz, a PeerJ Section Editor covering this Section #]
After reviewing the revised manuscript, I observed that the authors have diligently addressed all the comments and suggestions I provided earlier. Given the improvements and revisions made, I believe the paper is now in an acceptable state for publication.
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The reviewers have substantial concerns about this manuscript. The authors should provide point-to-point responses to address all the concerns and provide a revised manuscript with the revised parts being marked in different color.
The paper has been written in a clear and professional manner, using unambiguous language throughout. The authors have provided sufficient background and context for the proposed technique, including literature references and a detailed explanation of the methodology. The article follows a professional structure, including figures and tables to explain the algorithmic usage and results. The authors have also shared raw data, which adds to the transparency of the research. The results are based on real data, and the article provides a clear comparison of the proposed technique with existing approaches. The formal results include clear definitions of all terms and theorems, and detailed proof. Overall, the paper is well-written and provides a comprehensive analysis of the proposed technique.
the article discusses the proposed technique for identifying and assessing the risk of cardiac illnesses using the Cleveland data set. The authors have used real data to evaluate the validity of the approach and have compared the proposed technique with existing approaches. The article provides a clear explanation of the methodology and algorithmic usage, including figures and tables. The authors have also shared raw data, which adds to the transparency of the research. Overall, while there is no explicit mention of an experimental design, the article provides a comprehensive analysis of the proposed technique using real data and a clear comparison with existing approaches.
The validity of the findings in the article is supported by the use of real data from the Cleveland data set. The authors have used this data to evaluate the proposed technique for identifying and assessing the risk of cardiac illnesses and have compared it with existing approaches. The article provides a clear explanation of the methodology and algorithmic usage, including figures and tables. The authors have also shared raw data, which adds to the transparency of the research. The formal results include clear definitions of all terms and theorems, and detailed proof. The authors have also discussed the benefits of the proposed technique and its potential for legitimacy within the computational domain. Overall, the use of real data and the comprehensive analysis of the proposed technique support the validity of the findings in the article.
no comment
Regarding the experimental design, I found it well-structured and clearly described. The authors utilized the Cleveland data set for their heart-related diagnostic research. The data set consists of 303 individuals with heart disease, and includes 76 characteristics or parameters, of which 14 are relevant to the analysis. In spite of this, only nine of the most important attributes were selected for the purpose of diagnosing heart disease. Based on the FpNHse-set structure, these attributes were further categorized into their corresponding sub-parametric values using the suggested classification system.
The Cleveland data set was chosen for analysis since it is a well-known data set in the field and provides a great basis for studying heart health issues. There was a clear fit between the experimental setup and the study objectives, as well as a sound methodology for the study.
In terms of validity, the manuscript presented convincing results in terms of the validity of the findings. It has been reported that the authors have successfully applied the proposed algorithmic MADM method to the analysis of heart problems. In conclusion, the outcomes showed
promising potential in addressing the challenges associated with the domain in question. The results of the study were supported by a rigorous analysis of the data and thorough discussion of the findings.
Based on these considerations, I would recommend that the manuscript be accepted for publication in PeerJ Computer Science on the basis of these considerations. In my opinion, this research makes a significant contribution to the field, especially when it comes to the analysis of
heart problems, which is another important aspect of the research. I believe that the methodology used in this study is well defined, that the experimental design is sound, and that the results of the study are well supported by the data that has been presented.
It was written in formal English that was objective and unbiased so that it could be read and understood by all professionals in their respective disciplines. This was done to ensure that the article could be read and understood by all professionals. There is an adequate quantity of background information included here, as well as references that are appropriate. The conclusion, in addition to the introduction and the summary, all make sense and provide the facts in an accurate manner. In addition to the labeling, it also had an appropriate header, which meant that it fulfilled all of the requirements. It encompasses not only the outcomes but also the processes. The code is presented in a way that makes it possible for it to be carried out, and the results can be replicated in their totality. It is able to fulfill the requirements necessary to qualify for admittance into the technically suitable area.
It is a one-of-a-kind piece that falls within the 'airms and scope' of the journal for which you are writing, so you may feel confident submitting it. The issue of the investigation is crystal obvious, and as a result, the solution must be both topical and meaningful in order to satisfy this requirement. The solution also fills in the gap in the current body of literature, and comments should be made regarding how the work contributes to filling in that gap in the literature. This eliminates a gap that was previously present in the relevant literature. In addition to complying with the 'airms and scope' of the magazine, it also satisfies the ethical requirements that were outlined earlier in this paragraph. The procedures that must be followed in order to finish an assignment consist of not only in-depth explanations but also blueprints for simulations.
It is creative and unique, and the findings are presented in a manner that is both comprehensive and detailed. They are linked to the primary research questions, and the discussion is restricted to the findings that provide support for the aforementioned questions. It is simple to replicate the results of the study given that both the dataset and the code are exposed to the general public and made available for their usage. It has been subjected to a review that makes use of statistical approaches and procedures. In a manner that is comprehensible while still being succinct, the advantages of including this new body of literature in the canon that is already in place have been described.
If a larger dataset is employed, the model will be able to more accurately predict outcomes; conversely, the dataset instance size appears to be rather small. There was no visible source code that I could find.
The study introduces the FpNHse-set, a mathematical structure improving heart disease risk assessment by addressing shortcomings in existing research. The framework employs Sanchez's medical technique to correlate patients, medical experts, and fuzzy parameterized sub-parametric tuples using the Cleveland dataset. An accessible algorithm simplifies computations, ranks patients, and demonstrates the system's flexibility compared to existing methods.
1. However, for the medical technique part, it is not clear. For example, line 46, the three techniques could have deep clarification, why to combine these three.
2. line 128 main contribution. Point 6 is not as crucial as other points, maybe some detailed benefits could add here
1. Can you double check each definition equations. For example, line151, p and c don't have clear notes.
2. Line 189, elements introduction is in a mess, can you use some table to make it clear
1. For figure 4, representation of Alternatives needs more discussion here.
2. Table 8, the direction of arrows need some notes
3. line 386, Sanchez’s approach, why it is simple but effective.
This study proposes a new method for the medical diagnosis of heart disease by integrating the fuzzy parameterized neutrosophic hypersoft expert set (FpNHse-set) with a modified Sanchez's method. The utilization of FpNHse-set is emphasized for its ability to handle uncertainties and enhance the adaptability and reliability of the decision-making process. Furthermore, the study introduces a fuzzy parameterized degree-based setting to improve the accuracy of the findings by considering the inherent ambiguity in parameterized levels. The effectiveness of the FpNHse-set is evaluated using the Cleveland data set for heart disease. By combining the FpNHse-set with a modified Sanchez's method, the study aims to provide a more accurate assessment of the risk associated with cardiac illnesses.
The authors extensively discussed the background and related research, offering a comprehensive introduction to the topic. However, it would greatly enhance the manuscript if the authors could further elucidate the specific research gap in the field. Specifically, it would be beneficial to clearly outline the advantages of employing fuzzy set classification in comparison to conventional machine learning methods, and the specific circumstances in which it is most advantageous. This clarification would provide readers with a deeper understanding of the unique benefits and applicability of fuzzy set classification in the given context.
The experimental evaluation in this study was carried out on a relatively small sample size of only 6 patients. As a result, the limited sample size poses challenges in accurately assessing and comparing the performance of the proposed method. It would greatly benefit the manuscript if the authors could provide additional insights on the rationale behind selecting this sample size and discuss any potential implications it may have on the generalizability of the findings.
Furthermore, in order to strengthen the significance of the proposed method, it is recommended that the authors provide a clearer explanation of the evaluation metrics used. Additionally, it would be beneficial to explicitly highlight how the proposed method outperforms existing methods, specifying the specific areas or aspects in which it demonstrates superior performance.
The author presented a comprehensive and well-structured explanation of the mathematical functions and algorithms utilized in the study.
A few questions:
How is the parameterized degree of uncertainty determined and assigned in the proposed method?
Regarding the algorithm, is the number of matrices fixed, or is there an optimization or selection procedure involved? How are the parameter sets decided and optimized?
In Figure 4, could you please clarify the representation of the x and y axes?
Line 41, the sentences repeat.
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