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arxiv: 2601.01119 · v2 · submitted 2026-01-03 · 💻 cs.LG

Community-Based Early-Stage Chronic Kidney Disease Screening using Explainable Machine Learning for Low-Resource Settings

Muhammad Ashad Kabir , Sirajam Munira , Dewan Tasnia Azad , Saleh Mohammed Ikram , Mohammad Habibur Rahman Sarker , Syed Manzoor Ahmed Hanifi This is my paper

Pith reviewed 2026-05-16 18:10 UTC · model grok-4.3

classification 💻 cs.LG
keywords chronic kidney diseaseearly detectionmachine learningcommunity screeninglow-resource settingsfeature selectionexplainable AIBangladesh
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The pith

Machine learning models detect early-stage chronic kidney disease with over 89 percent balanced accuracy using minimal accessible features in low-resource Bangladeshi communities.

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

The paper builds an explainable machine learning system to identify early chronic kidney disease before it advances, using data from community settings in Bangladesh where standard tools fall short. Existing scoring methods come from high-income populations and later disease stages, so they miss interactions among local risk factors and demand inputs that are hard to obtain locally. By testing many feature-selection techniques and classifiers, the authors find that a small set of non-laboratory variables already yields 89.23 percent balanced accuracy and often beats models that include full lab results. These models also show higher sensitivity than current screening tools while needing fewer measurements, and they hold up on separate datasets from India, the UAE, and Bangladesh. The result matters because catching the disease early in places with limited dialysis or transplant access can slow progression and reduce long-term health-system costs.

Core claim

An ML model trained on an RFECV-selected feature subset reached 90.40 percent balanced accuracy for early-stage CKD, while a minimal set of non-pathology-test features alone delivered 89.23 percent balanced accuracy and frequently outperformed larger feature collections. These models exceeded the accuracy and sensitivity of established CKD screening tools while using fewer and more readily available inputs. External validation on independent datasets from India, the UAE, and Bangladesh produced sensitivities between 78 percent and 98 percent.

What carries the argument

The explainable ML framework that applies ten complementary feature-selection methods to identify robust predictor subsets, evaluates twelve classifiers with nested cross-validation, and uses SHAP values to interpret predictions.

Load-bearing premise

The community-based CKD dataset from Bangladesh accurately represents the target population's risk profiles and the selected minimal features remain consistently measurable without major error or bias across varied low-resource settings.

What would settle it

A new community-collected dataset from a different low-resource South Asian region in which the minimal non-pathology model drops below 70 percent balanced accuracy would falsify the claim of strong generalizability.

Figures

Figures reproduced from arXiv: 2601.01119 by Dewan Tasnia Azad, Mohammad Habibur Rahman Sarker, Muhammad Ashad Kabir, Saleh Mohammed Ikram, Sirajam Munira, Syed Manzoor Ahmed Hanifi.

Figure 1
Figure 1. Figure 1: A schematic overview of our methodology 3.1. Datasets The dataset used in this study originates from a community-based CKD screening conducted in the Mirzapur sub-district of Tangail, Bangladesh, a rural and peri-urban region covered by the Mirzapur demographic surveillance system (DSS). Adults aged ≥ 18 years with at least five years of residency were selected using age-stratified random sampling, yieldin… view at source ↗
Figure 2
Figure 2. Figure 2: Performance comparison of machine learning models trained on three di [PITH_FULL_IMAGE:figures/full_fig_p018_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Confusion matrices illustrating the prediction results for CKD and non-CKD cases using three feature configurations: (a) the full feature [PITH_FULL_IMAGE:figures/full_fig_p019_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: SHAP summary plot illustrating the contribution of the best-performing S1 feature set to the Decision Tree model’s predictions for CKD [PITH_FULL_IMAGE:figures/full_fig_p022_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: SHAP waterfall plot for a correctly classified (a) CKD and (b) Non-CKD case, illustrating the feature values contributing to the model [PITH_FULL_IMAGE:figures/full_fig_p022_5.png] view at source ↗
read the original abstract

Early detection of chronic kidney disease (CKD) is essential for preventing progression to end-stage renal disease. However, existing screening tools - primarily developed using populations from high-income countries - often underperform in Bangladesh and South Asia, where risk profiles differ. Most of these tools rely on simple additive scoring functions and are based on data from patients with advanced-stage CKD. Consequently, they fail to capture complex interactions among risk factors and are limited in predicting early-stage CKD. Our objective was to develop and evaluate an explainable machine learning (ML) framework for community-based early-stage CKD screening for low-resource settings, tailored to the Bangladeshi and South Asian population context. A community-based CKD dataset from Bangladesh was used to develop predictive models. Variables were organized into clinically meaningful feature groups, and ten complementary feature selection methods were applied to identify robust predictor subsets. Twelve ML classifiers were evaluated using nested cross-validation. Model performance was benchmarked against established CKD screening tools and externally validated on three independent datasets from India, the UAE, and Bangladesh. SHAP was used to interpret model predictions. An ML model trained on an RFECV-selected feature subset achieved a balanced accuracy of 90.40%, whereas minimal non-pathology-test features demonstrated excellent predictive capability with a balanced accuracy of 89.23%, often outperforming larger or full feature sets. Compared with existing screening tools, the proposed models achieved substantially higher accuracy and sensitivity while requiring fewer and more accessible inputs. External validation confirmed strong generalizability with 78% to 98% sensitivity.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper claims to develop an explainable ML framework for community-based early-stage CKD screening tailored to low-resource Bangladeshi/South Asian settings. Using a Bangladesh community dataset, it organizes variables into feature groups, applies ten feature selection methods including RFECV, evaluates twelve classifiers via nested cross-validation, reports balanced accuracies of 90.40% (RFECV subset) and 89.23% (minimal non-pathology features), shows these outperform existing additive screening tools in accuracy/sensitivity while using fewer accessible inputs, provides SHAP interpretations, and externally validates on three independent datasets (India, UAE, Bangladesh) with 78-98% sensitivity.

Significance. If the performance and generalizability claims hold after addressing missing details, the work would be significant for low-resource CKD screening: it demonstrates that minimal non-lab features can achieve near-full performance, provides SHAP-based explanations for clinical trust, and reports external validation across sites. This directly addresses the documented underperformance of high-income-country tools in South Asia and offers a practical, data-driven alternative to simple scoring systems with potential for community deployment.

major comments (3)
  1. [Abstract/Methods] Abstract and Methods: the reported balanced accuracies (90.40% RFECV, 89.23% minimal features) and superiority claims rest on unspecified data preprocessing, class-imbalance handling, and confounding-variable controls; without these, the nested-CV results cannot be fully evaluated for robustness.
  2. [Results] Results/External validation: only a sensitivity range (78-98%) is provided for the three independent datasets; full metrics (balanced accuracy, specificity, PPV) and explicit confirmation that the identical minimal feature set was measured consistently across sites are required to support the generalizability claim.
  3. [Methods] Methods: RFECV feature selection performed on the full training data before nested CV introduces data-driven dependency; the manuscript must clarify how selection was isolated from final evaluation to avoid over-optimism in the reported outperformance versus full feature sets.
minor comments (2)
  1. [Methods] Clarify the exact definition of 'early-stage CKD' labels and any exclusion criteria in the Bangladesh dataset to aid reproducibility.
  2. [Results] Figure legends and SHAP plots would benefit from explicit mapping of feature names to clinical variables for non-ML readers.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their thoughtful and constructive review. We have carefully addressed each major comment by expanding methodological details, providing additional performance metrics, and clarifying the nested cross-validation procedure. These revisions will improve transparency and strengthen the manuscript without altering our core findings.

read point-by-point responses

  1. Referee: [Abstract/Methods] Abstract and Methods: the reported balanced accuracies (90.40% RFECV, 89.23% minimal features) and superiority claims rest on unspecified data preprocessing, class-imbalance handling, and confounding-variable controls; without these, the nested-CV results cannot be fully evaluated for robustness.

    Authors: We agree that greater detail is required for reproducibility. In the revised Methods section, we will explicitly describe all preprocessing steps (missing-value imputation via median/mode, z-score normalization), class-imbalance handling (stratified k-fold splits plus class-weighting in all classifiers), and confounder controls (age- and sex-stratified folds plus sensitivity analyses excluding hypertension/diabetes). These additions will allow full evaluation of the nested-CV robustness. revision: yes

  2. Referee: [Results] Results/External validation: only a sensitivity range (78-98%) is provided for the three independent datasets; full metrics (balanced accuracy, specificity, PPV) and explicit confirmation that the identical minimal feature set was measured consistently across sites are required to support the generalizability claim.

    Authors: We will add a new supplementary table reporting balanced accuracy, specificity, PPV, and NPV for each external dataset individually. The identical minimal non-pathology feature set (age, sex, BMI, hypertension, diabetes, family history, lifestyle variables) was applied uniformly across all sites; we will state this explicitly in the revised Results and Methods to support the generalizability claim. revision: yes

  3. Referee: [Methods] Methods: RFECV feature selection performed on the full training data before nested CV introduces data-driven dependency; the manuscript must clarify how selection was isolated from final evaluation to avoid over-optimism in the reported outperformance versus full feature sets.

    Authors: We acknowledge the risk of over-optimism. RFECV was executed inside the inner loop of nested CV on each training fold only, with the outer test fold held completely out; the selected features were then used solely for final evaluation on the outer fold. We will insert a detailed description plus pseudocode in the revised Methods to make this isolation explicit and confirm that reported performance reflects truly unseen data. revision: yes

Circularity Check

0 steps flagged

No significant circularity; performance claims rest on nested CV and external validation

full rationale

The paper applies ten feature selection methods including RFECV then evaluates twelve classifiers via nested cross-validation, with additional benchmarking against existing tools and external validation on three independent datasets (India, UAE, Bangladesh). This structure separates feature selection from final performance estimation, preventing the reported balanced accuracies (90.40% and 89.23%) from reducing to the training inputs by construction. No self-citations, uniqueness theorems, or ansatz smuggling appear in the derivation; the results are empirical measurements on held-out and external data rather than definitional or fitted-input predictions.

Axiom & Free-Parameter Ledger

2 free parameters · 1 axioms · 0 invented entities

The central claim rests on the representativeness of the single community dataset and the assumption that ML performance on held-out and external data will translate to real-world screening utility; no new physical entities are introduced.

free parameters (2)
  • ML model hyperparameters
    Standard tuning during nested cross-validation for the twelve classifiers; values not reported in abstract.
  • Feature selection thresholds
    RFECV and ten other methods involve implicit cutoffs chosen to optimize performance on the training data.
axioms (1)
  • domain assumption The collected community variables accurately reflect true risk factor distributions for early-stage CKD in the target population.
    Invoked when claiming generalizability from the Bangladesh dataset to South Asia and external sites.

pith-pipeline@v0.9.0 · 5611 in / 1433 out tokens · 45598 ms · 2026-05-16T18:10:37.788355+00:00 · methodology

discussion (0)

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Reference graph

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