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REVIEW 4 major objections 4 minor 1 cited by

A Structured Dataset of Disease-Symptom Associations to Improve Diagnostic Accuracy

T0 review · 4 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read A structured Bangla disease-symptom dataset of 85 diseases and 172 symptoms supports disease classifiers that reach 0.97 accuracy.

desk verdict A useful Bangla disease-symptom dataset, but the paper's diagnostic-accuracy claim rests on a circular evaluation and an unresolved contradiction in how the data were sourced. read the letter →

arxiv 2506.13610 v5 pith:BBY3EO7A submitted 2025-06-16 cs.CL

Abdullah Al Shafi , Rowzatul Zannat , Abdul Muntakim , Mahmudul Hasan This is my paper · ORCID
classification cs.CL
keywords disease-symptomdatasetBanglamedicaldatadiseaseclassificationAIinhealthcareclinicaldatasetsinformaticspredictivemodelingbinarysymptommatrix
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper introduces a structured tabular dataset that records, for each of 85 diseases, which of 172 symptoms are associated with it, encoded as binary 1/0 entries and covering 758 disease-symptom relationships. Its aim is to fill a concrete gap: no comparable structured disease-symptom resource in Bangla was available for machine-learning-based diagnosis, and prior Bangla resources were machine-translated from an English dataset. If the associations are valid, the dataset provides a reusable training and benchmark resource for Bangla medical NLP, symptom-based prediction, clinical decision support, and epidemiological surveillance. The authors report that logistic regression, random forest, and perceptron classifiers trained on this matrix reach 0.97 accuracy, with logistic regression showing the best precision, recall, and F1-score.

What carries the argument

The load-bearing object is a binary disease-symptom matrix: rows are 85 diseases, columns are 172 symptoms, and each cell holds 1 if the symptom commonly appears in that disease and 0 otherwise. This matrix simultaneously defines the dataset's content and the feature representation for the classification experiments, because the 172 binary columns are fed directly into standard models (perceptron, logistic regression, naive Bayes, decision tree, k-nearest neighbours, passive-aggressive classifier, random forest, and support vector machine) whose reported scores carry the accuracy claim.

What would settle it

Have independent clinicians review a random sample of the 758 disease-symptom entries against standard diagnostic references, then evaluate the same classifiers on symptom presentations drawn from real patient records; if the associations fail review or accuracy drops on real presentations, the dataset's diagnostic claim is not established.

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Extended reading notes

Core claim

The central claim is that a manually compiled binary disease-symptom matrix can serve as a valid structured resource for Bangla healthcare informatics. The paper asserts that this matrix bridges the absence of structured Bangla disease-symptom data and that standard machine-learning classifiers can use it to predict diseases from symptoms with high accuracy, with the best models at 0.97 accuracy. The resource is offered in raw, cleaned, and English-translated forms so that it can support symptom co-occurrence analysis, classifier training, benchmarking, syndromic surveillance, and cross-lingual medical research.

Load-bearing premise

The dataset's value rests on the manual assignment of which symptoms belong to which diseases being medically correct, even though the paper reports no inter-annotator agreement, independent clinical audit, or explicit threshold for 'appeared commonly.'

Editorial extensions

If this is right

  • If the dataset is correct, Bangla healthcare NLP no longer needs to rely on machine-translated English symptom data; classifiers can be trained and benchmarked on a native binary matrix.
  • The matrix format directly supports symptom co-occurrence and clustering analysis across 85 diseases, which the paper identifies as useful for diagnosis and public-health surveillance.
  • The 758 curated relationships give later Bangla medical datasets a baseline to extend with region-specific diseases and updated symptom associations.
  • The reported accuracy across several model families indicates the dataset is stable enough for classification tasks and not merely a descriptive compilation.
  • The English-translated version creates a path for comparing Bangla symptom naming with English disease-symptom resources in multilingual informatics work.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • I would read the 0.97 accuracy as a measure of internal consistency as long as the symptom associations themselves are the labels; the decisive next step is independent clinical validation of the association table.
  • The dominance of generic symptoms such as headache, nausea, and fever within the matrix suggests that co-occurrence-based or multi-label modeling will extract more diagnostic signal than single-symptom rules, a direction the paper mentions but does not develop.
  • The paper's own limitation section concedes that rare and local diseases are underrepresented, so the accuracy claim may not transfer to primary-care settings in Bangladesh where those conditions are common; extending the table with region-specific diseases is the natural next test.
  • The binary format with English translations also invites a transfer experiment: align this table with an English symptom dataset and measure whether native Bangla symptom wording adds diagnostic value over a direct translation.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. This data descriptor introduces a Bangla-language disease-symptom dataset containing 85 diseases, 172 symptoms, and 758 binary associations, released on Mendeley Data with DOI 10.17632/rjgjh8hgrt.5. The paper describes data collection, cleaning, feature reduction, and reports machine-learning classification results, with the highest accuracy of 0.97 (Table 3), as evidence that the dataset improves diagnostic accuracy. The stated goal is to fill a gap in structured Bangla medical data and support multilingual medical informatics tools.

Significance. If the disease-symptom associations were medically validated, this dataset would fill a real gap in structured Bangla medical NLP resources. The authors provide a downloadable, tabular artifact that includes cleaned data and English translations, which is a useful starting point for downstream research. However, the paper currently does not establish the medical validity of the associations or the claimed improvement in diagnostic accuracy; the reported evaluation is internal and self-referential. The artifact is potentially reusable, but the central claims require substantial additional evidence or a careful reframing of the dataset's scope.

major comments (4)
  1. [Abstract and Methods (Data collection and annotation)] The Abstract and Specifications Table state that only verified medical sources were included and that non-peer-reviewed or anecdotal sources were excluded, but the Methods section 'Data collection and annotation' explicitly lists 'Bangla blogs, Bangla newspapers, online surveys' among the sources used for symptom-to-disease mapping. These are exactly non-peer-reviewed and anecdotal sources, so the inclusion criteria are internally contradictory and the provenance of the 758 associations is unclear.
  2. [Methods (Binary Encoding)] The Binary Encoding step assigns a symptom value of 1 if the symptom 'appeared commonly' in a disease, but no quantitative threshold, named expert, reference standard, or inter-annotator agreement is reported. The paper also does not compare the resulting matrix to an established clinical source or an external gold standard. Without this information, the correctness of the dataset cannot be assessed, and any downstream classification result inherits this uncertainty.
  3. [Table 3 and Experimental Design] Table 3 reports accuracy up to 0.97 but cites the authors' prior conference paper [10] and gives no details in this manuscript about train/test splits, cross-validation, class balance, hyperparameters, or baselines. Because classifiers are trained and tested on the same manually constructed matrix, these numbers primarily measure internal consistency of the encoding procedure rather than diagnostic accuracy on real patient data. External validation against clinical records or a previously published disease-symptom benchmark is needed to support the claim of improved diagnostic accuracy.
  4. [Limitations] The Limitations section addresses incompleteness and lack of real-time updates, but it does not discuss the possibility that some disease-symptom associations are incorrectly labeled. This is the central threat to the dataset's value. The authors should either provide validation checks (for example, clinician review or comparison with standard references) or explicitly scope the claims to a resource of curated associations requiring further validation.
minor comments (4)
  1. [Figures] Figure numbering is inconsistent: Fig. 2 is missing, and Fig. 5 is used both for the dataset snapshot and for the development procedure.
  2. [Background and References] In the Background section, the DSR work by Zlabinger et al. is cited as [3], but in the reference list [3] is Arbatti et al.; reference [4] is the Zlabinger paper.
  3. [Data Cleaning] The data cleaning step that fills in gaps 'where practicable' is not documented per record, making it impossible to distinguish directly curated values from estimated or imputed ones.
  4. [Specifications Table] The 'Related research article' field says 'none' despite reference [10] reporting machine-learning evaluation of this dataset; the relationship should be clarified.

Circularity Check

2 steps flagged · score 6.0 of 10

The headline diagnostic-accuracy claim is self-referential: the 0.97 accuracy is measured on the same manually constructed symptom matrix and is sourced from the authors' own prior conference paper, so it demonstrates internal consistency, not external diagnostic utility.

  1. fitted input called prediction [Methods: Feature Reduction and Standardization; Table 3]
    "Some symptoms appeared in almost all diseases, hence being irrelevant in distinguishing between them. Similarly, some symptoms appeared in very few diseases to be of any use in prediction. These were either eliminated or combined, so only relevant symptoms remained in the dataset. This selection procedure reduced noise and improved the overall accuracy of the classification models."

    Symptom features are selected or removed based on how well they separate the disease classes within the very dataset under evaluation, and the resulting 'improved accuracy' is then reported as evidence that the dataset supports disease classification. Since the class labels and the selected feature set arise from the same manual construction process, the reported 0.97 accuracy is a measure of internal consistency of the constructed matrix rather than a predictive result against independent diagnostic ground truth.

  2. self citation load bearing [Table 3 caption; References [10]]
    "Table 3 Performance of various Machine Learning models on disease classification with the proposed dataset [10]:"

    The only quantitative support for the paper's core claim of 'improving diagnostic accuracy' is Table 3, which cites reference [10], a 2025 ECCE paper co-authored by R. Zannat, A. Al Shafi, and A. Muntakim — three of the present authors. The cited paper evaluates classifiers on the same manually constructed dataset, so this self-citation is load-bearing but does not provide independent external validation; it merely re-imports the same in-sample evaluation.

full rationale

This is not a mathematical derivation, so there are no equations that reduce to one another by construction. The circularity is empirical and evidential: the paper's central claim that the dataset 'improves diagnostic accuracy' is supported by the 0.97 classification accuracy in Table 3, but that accuracy is obtained by training and testing models on the same manually curated disease-symptom matrix. The Methods section explicitly states that symptom features were eliminated or combined because they were not useful for distinguishing between diseases before the classifiers were run, meaning the feature set was constructed using the target labels in the same dataset. The accuracy therefore mostly reflects how well the authors' own binary associations are internally reproducible, not whether the associations match real clinical diagnoses. Moreover, Table 3 is attributed to the authors' prior conference paper [10], so the quantitative evidence for the central claim is a self-citation rather than an external benchmark. The dataset itself — 85 diseases, 172 symptoms, 758 binary relations — is a tangible artifact whose reuse value could be genuine, which prevents a score of 8 or 10. However, the paper does not compare against any external or clinically validated disease-symptom resource, and the Limitations section concedes incompleteness but never addresses label accuracy, which is the actual threat to the dataset's validity. The contradiction between the abstract's claim that only verified medical sources were used and the Methods' mention of Bangla blogs, online surveys, and newspapers is a source-verification problem, not circularity, and was not counted in the score. Overall, the central diagnostic-improvement claim is partially circular because its main evidence is self-referential and self-cited.

Assumptions & free parameters 1 free parameters · 3 assumptions · 0 invented entities

The paper introduces no new theoretical entities or forces. Its epistemic burden lies in the annotation process: the binary matrix is built from subjective manual curation, and the ML evaluation is self-referential. The central claim relies on the assumption that the manually assigned labels are correct and that testing on them measures diagnostic value.

free parameters (1)
  • symptom assignment threshold ('appeared commonly') = not specified
    The binary labeling depends on a subjective judgment of which symptoms are 'common' in a disease, with no quantitative threshold or validation. This choice directly shapes the entire dataset and the resulting classifier performance.
assumptions (3)
  • domain assumption Disease-symptom associations can be adequately encoded as binary presence/absence indicators.
    The dataset represents each symptom for each disease as 1 or 0, ignoring symptom frequency, severity, and interactions. The cited DSR dataset [3] uses graded judgments instead, which suggests binary encoding is a simplification. Location: Data Description.
  • domain assumption The cited online sources (blogs, newspapers, surveys, 'expert medical knowledge') are accurate enough to define ground truth labels.
    The Methods section says data came from these sources, while the abstract claims only peer-reviewed sources were used. No source list or expert credentials are provided, so the reliability of the ground truth is assumed. Location: Experimental Design, Materials and Methods, Data collection and annotation.
  • ad hoc to paper Classifier accuracy on a train/test split of this dataset measures the dataset's value for diagnostic accuracy.
    The evaluation trains and tests on the same manually constructed dataset, with no external clinical or benchmark validation. High accuracy may simply reflect internal consistency of the labels, not real-world diagnostic improvement. Location: Table 3.

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Cite this review

Pith. "Pith review of A Structured Dataset of Disease-Symptom Associations to Improve Diagnostic Accuracy." pith.science (2026). https://pith.science/paper/BBY3EO7A

@misc{pith2026250613610,
  author       = {Pith},
  title        = {Pith review of: A Structured Dataset of Disease-Symptom Associations to Improve Diagnostic Accuracy},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/BBY3EO7A}},
  note         = {Machine review of arXiv:2506.13610}
}
read the original abstract

Disease-symptom datasets are significant and in demand for medical research, disease diagnosis, clinical decision-making, and AI-driven health management applications. These datasets help identify symptom patterns associated with specific diseases, thus improving diagnostic accuracy and enabling early detection. The dataset presented in this study systematically compiles disease-symptom relationships from various online sources, medical literature, and publicly available health databases. The data was gathered through analyzing peer-reviewed medical articles, clinical case studies, and disease-symptom association reports. Only the verified medical sources were included in the dataset, while those from non-peer-reviewed and anecdotal sources were excluded. The dataset is structured in a tabular format, where the first column represents diseases, and the remaining columns represent symptoms. Each symptom cell contains a binary value, indicating whether a symptom is associated with a disease. Thereby, this structured representation makes the dataset very useful for a wide range of applications, including machine learning-based disease prediction, clinical decision support systems, and epidemiological studies. Although there are some advancements in the field of disease-symptom datasets, there is a significant gap in structured datasets for the Bangla language. This dataset aims to bridge that gap by facilitating the development of multilingual medical informatics tools and improving disease prediction models for underrepresented linguistic communities. Further developments should include region-specific diseases and further fine-tuning of symptom associations for better diagnostic performance

Discussion (0). Continue with ORCID to comment.

Forward citations

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Reference graph

Works this paper leans on

9 extracted references · 8 canonical work pages · cited by 1 Pith paper

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    A Structured Bangla Dataset of Disease-Symptom Associations to Improve Diagnostic Accuracy

    Ratul, Abdullah Al Shafi; Zannat, Rowzatul; Muntakim, Abdul; Hasan, Mahmudul (2025), “A Structured Bangla Dataset of Disease-Symptom Associations to Improve Diagnostic Accuracy”, Mendeley Data, V3, doi: 10.17632/rjgjh8hgrt.5

    2025 doi

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Reviewed August 7, 2026 · model on record in the stance chip above.