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REVIEW 5 major objections 7 minor 1 cited by

CACTUS as a Reliable Tool for Early Classification of Age-related Macular Degeneration

T0 review · 5 major / 7 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read This paper claims that CACTUS, a graph-based explainable classifier, stages age-related macular degeneration from non-imaging data with higher balanced accuracy than six standard machine-learning models across every tested level of…

desk verdict A genuine extension of the authors' CACTUS method with an internally consistent head-to-head win over six ML baselines, but the central comparison may be in-sample because the abstraction and graph parameters are never described as cross-validated per fold. read the letter →

arxiv 2506.14843 v1 pith:4S3LF5FU submitted 2025-06-16 cs.LG cs.CVstat.AP

Luca Gherardini , Imre Lengyel , Tunde Peto , Caroline C.W. Klaverd , Magda A. Meester-Smoord , Johanna Maria Colijnd , EYE-RISK Consortium , E3 Consortium
show 1 more author
Jose Sousa
This is my paper · ORCID
classification cs.LGcs.CVstat.AP
keywords Age-relatedmaculardegenerationExplainableAIKnowledgegraphsClassificationMissingdataBalancedaccuracyCACTUSPageRank
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

This paper tries to establish that a knowledge-graph classifier called CACTUS can stage age-related macular degeneration (AMD) using ordinary clinical variables, such as age, genetics, diet, and comorbidity, without retinal images. The authors argue that CACTUS beats six standard machine-learning baselines in balanced accuracy at every level of induced missing data, and that its PageRank variant offers the most usable confidence estimates for real screening. If true, that would matter because AMD progression is slow but irreversible, and early screening on fragmented primary-care data could flag patients at risk before sight loss. The paper also claims the features CACTUS relies on, especially age and genetic risk score, align with established medical knowledge, which supports its transparency story.

What carries the argument

The load-bearing object is the per-class knowledge graph: a weighted directed graph whose nodes are abstracted flips (Up and Down for continuous features, allele categories for SNPs) and whose edges are the absolute difference between a conditional flip probability and 0.5, encoding how much more likely two flips are to co-occur than chance. Graph centrality, computed with PageRank or total degree, is multiplied by each flip's class-specific probability to give a significance score; a patient's similarity to a class is the sum of those significances over the flips they show. The new confidence measure, the average absolute difference between the winning class score and the other class scores, is what the paper uses to claim that the PageRank variant is the most trustworthy of the three CACTUS modes.

What would settle it

Train CPR and the same baselines on a dataset built from each patient's first visit, or on a simulated cohort with no dropout, and compare balanced accuracy and top-ranked features; if CPR's advantage over XGBoost shrinks or age loses its top rank, the paper's reliability claim for early screening is not supported. Separately, check whether CPR's confidence values actually order patients by true accuracy within each stage; if they do not, the trust-from-confidence argument fails.

Watch

Extended reading notes

Core claim

On the paper's own terms: CACTUS abstracts each continuous feature into Up and Down flips using per-feature ROC cut-offs over all class partitions, builds one weighted directed knowledge graph per AMD stage whose edges are the absolute distance of conditional flip probabilities from 0.5, and scores new patients by combining flip probabilities with graph centrality (Probabilistic, Degree, or PageRank). The central result is that on 29,908 last-visit records from the harmonised EYE-RISK dataset, CACTUS's PageRank variant reaches a balanced accuracy of 0.34 for the five AMD stages, the highest of any model and the best in 3 of 5 fragmentation experiments, while the standard baselines stay between 0.27 and 0.31. Because random guessing is 0.20, the paper interprets these numbers as evidence that the graph representations capture genuine stage-specific structure in non-imaging data, and that the accompanying confidence score lets clinicians decide when to trust a decision.

Load-bearing premise

The results assume that picking each patient's last recorded visit produces a representative and balanced sample of a real early-screening population, so 25 years of dropout does not systematically distort which features predict AMD stage.

Editorial extensions

If this is right

  • If CACTUS's PageRank classification holds up, AMD staging can be performed from longitudinal questionnaire and lab data alone, reducing dependence on specialist image grading for screening.
  • The confidence metric allows clinicians to defer decisions when confidence is low, concentrating scarce resources on the patients the model is least sure about.
  • The feature ranks provide a transparent, testable bridge to medical literature: age topping the ranks and genetic scores appearing near the top are the kinds of outputs clinicians can verify.
  • The finding that nine features capture most of the model's performance suggests that a minimal data-collection protocol could be sufficient for early screening.
  • Because balanced accuracy stays modest (0.34) even though it beats baselines, the intended use is risk triage to aid a clinician, not autonomous diagnosis.

Reading between the lines

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

  • CPR's edge over XGBoost at higher fragmentation rates suggests the abstraction step may be acting as a noise filter; a direct test would be to compare CACTUS against an XGBoost model trained on discretized features to isolate the contribution of graph centrality.
  • The confidence-population curves could be repurposed as a general calibration tool: any classifier can be evaluated by how its accuracy changes when you restrict to high-confidence cases, with CPR serving as a baseline rather than a special case.
  • Because the dataset is built from each patient's last visit, the reported 'early classification' performance is measured on the most advanced visit per person; a direct test for early diagnosis would use first visits or time-to-conversion to late AMD as the outcome.
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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

5 major / 7 minor

Summary. The manuscript applies CACTUS, a knowledge-graph-based explainable classifier, to stage age-related macular degeneration (AMD stages 0–4) using non-imaging data from the EYE-RISK/E3 consortium (29,908 patients, 218 features). The method discretizes continuous features via an exhaustive ROC-based abstraction, builds class-specific weighted directed graphs, and assigns labels through three variants: Probabilistic (CPB), Degree (CDG), and PageRank (CPR). The authors compare these variants against six standard ML algorithms (Ridge, RF, LR, SGD, SVM, XGB) at 0%, 20%, 40%, 60%, and 80% artificially induced missing values, reporting that CACTUS variants achieve higher balanced accuracy than all baselines at every fragmentation level (Table 2). They introduce a confidence metric, rank features, and then refine the feature pool and evaluate the top-9 features (Table 3, Figures 4–7). The paper claims that CACTUS, especially CPR, is a reliable explainable tool for early AMD classification.

Significance. If the evaluation is unbiased, the paper demonstrates a transparent, missingness-robust alternative to image-based AMD staging: CACTUS is explainable, provides a confidence metric, and its feature rankings recover established AMD risk factors such as age, ARMS2, CFH, and genetic scores. Comparing against XGBoost is a useful addition. However, absolute balanced accuracy is modest (at most 0.34 versus a 0.20 chance level), the study is purely retrospective with substantial dropout, there is no external validation, and the reported comparisons carry a material risk of in-sample evaluation. The practical utility for early screening is therefore not established by this manuscript alone.

major comments (5)
  1. [Methods: 'Abstractions', 'The Knowledge Graphs', and 'Classification'; Figure 1] The paper does not state whether CACTUS's fitted components—the exhaustive ROC-based cut-off search per continuous feature, the class-partition selection, and the conditional-probability graph weights—are recomputed inside each training fold. The Classification section and Figure 1 say the model is built and 'the classification is then applied to all the available individuals to assess the performance,' while the ML baselines are described with an 80/20 split and 10-fold cross-validation. If the flips and graph parameters are estimated on the full data before scoring, the test labels have influenced the feature discretization and graph structure, so the Table 2 advantage (e.g., CPR 0.34 vs XGB 0.31 at 0% missingness) could be leakage-driven. The authors must either provide a nested evaluation in which all fitted parameters are estimated on training folds only and applied to held-out folds, or otherwise demonstrate that the comparison is not in-sample.
  2. [Results: 'CACTUS ranks'; Table 3] The refinement step selects the nine highest-ranked features from ranks computed on the full dataset and then reports balanced accuracy on the same data. Because feature selection and evaluation share the same samples, the Table 3 results (CDG 31%, CPB 29%, CPR 29% on the nine highest ranks) are at risk of selection bias and cannot be interpreted as unbiased performance estimates. The authors should perform feature selection inside an inner cross-validation loop or on a separate validation cohort, and then evaluate on an untouched test portion.
  3. [Methods: 'The defined dataset' paragraph and Table 1] The dataset is constructed from the last available visit per patient ('To have both a populous and balanced dataset, we considered the last visit available for each person'), and Table 1 shows heavy dropout over the study (from 28,910 participants at visit 1 to 1,538 at visit 5, with 17,526 dropped by visit 2). If dropout is correlated with disease severity, health status, or age, the resulting cohort may not represent an early-screening population, and the reported accuracy and the top ranking of age as a feature may be inflated. The manuscript should discuss this selection mechanism and its possible directional bias, or provide sensitivity analyses stratified by visit completeness.
  4. [Results: Table 2 and Discussion] The central performance comparison is reported only as mean ± standard deviation, with no statistical significance tests, confidence intervals, or paired comparisons across cross-validation folds. Given the small absolute differences (e.g., 0.34±0.01 vs 0.31±0.01 at 0% missingness) and overlapping standard deviations, the claim that CACTUS 'outperforms' the baselines is not statistically supported by the evidence as presented. Please add appropriate significance testing, such as paired tests on the cross-validation folds or bootstrap confidence intervals for the differences.
  5. [Results: 'CACTUS ranks' versus Table 3] The text states 'This filtration decreased the balanced accuracy by 0% to 2% for CDG, CPR, and CPB, as shown in Table 3,' but Table 3 reports CDG dropping from 33% (whole dataset) to 26% (refined dataset), a seven-percentage-point decrease. This internal inconsistency between the stated claim and the displayed results must be corrected and, if the refined-dataset CDG value is accurate, the discussion of the refinement step should be revised accordingly.
minor comments (7)
  1. [Figure 2 legend] The figure legend labels the model 'Linear Regression (LR),' while the Methods section refers to 'Logistic Regression (LR);' the labels should be made consistent.
  2. [Methods: 'Comparison against standard Machine Learning algorithms'] A stray character 'Z' appears after 'Logistic Regression (LR)' in the text; please remove it.
  3. [Table 2 caption] The caption says the balanced accuracy is reported 'in percentage,' but the table entries are decimals (e.g., 0.33, not 33); either the caption or the entries should be adjusted for consistency.
  4. [Results: 'CACTUS confidence'] The phrase 'The more inflated the cumulative lines for confidence and populations, the better the corresponding model' is difficult to parse and should be rewritten for clarity.
  5. [Results: 'CACTUS confidence' and Figure 3] The text mentions a 'confidence threshold of 15%' that 'enables us to confidently apply CPR on 50% of the population,' but Figure 3 annotates vertical lines as the confidence levels covering 90%, 80%, 70%, 60%, and 50% of the population; please clarify which line corresponds to the 15% threshold or adjust the text.
  6. [Methods: 'Comparison against standard Machine Learning algorithms'] The statement that CACTUS 'does not make any assumptions (weak model) on how the real process works' is vague, and the citations to White and Hellström do not directly justify the overfitting argument; please provide a more precise complexity comparison (e.g., number of effective parameters versus training sample size).
  7. [General] No data or code availability statement is provided; a clear statement on code and trained model availability would be important for reproducibility of the main comparison.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity: the CACTUS equations are self-contained and the listed self-citation is not load-bearing; the in-sample evaluation concern is a protocol-transparency issue, not a demonstrated circularity.

full rationale

The paper's derivation chain is not circular in the definitional sense. Equations (1)-(4) define conditional-probability graph weights, centrality, rank, and confidence from the data; none of them assumes the conclusion that CACTUS classifies AMD stages well. The abstraction thresholds are chosen by ROC search over the classes, but this is a supervised fitting procedure, not a circular prediction; if the evaluation were held out, the fitted values would not force the reported balanced accuracy. The only possible circularity would be evaluating CACTUS on the same data used to build its graphs. The text does contain a sentence in Figure 1 saying the classification is applied to all available individuals, but Table 2 explicitly states that all models were tested with a 10-fold cross-validation and an 80/20 split, so the paper as a whole does not unambiguously demonstrate a resubstitution-only evaluation. An underspecified CACTUS-specific CV protocol is a reproducibility and correctness risk, not a shown circular step. The citation of the authors' prior CACTUS paper [23] is a normal self-citation: the current manuscript re-describes the algorithm in sufficient detail (abstractions, knowledge graphs, centrality, rank, confidence), so the central claim does not rest on the cited paper's authority. No uniqueness theorem, ansatz-by-citation, or renaming of a known result is present.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The central claim is an empirical model-performance claim, not a derivation, so the ledger records data-driven modeling choices and domain assumptions. The load-bearing choices are the per-feature cutoffs, the class-partition search, the 50% missing-value filter, and the retrospective last-visit sampling; the most consequential unverified assumption is that this last-visit cohort represents an early-screening population.

free parameters (3)
  • Per-feature ROC cut-off thresholds (Up/Down flips) = One optimal threshold per continuous feature, not listed in paper
    Each continuous feature is dichotomized at the threshold that maximizes balanced accuracy when separating classes (Methods, 'Abstractions'); these thresholds are selected on the data and drive all downstream graphs, rankings, and classifications.
  • Class partition choice per feature during abstraction = Exhaustive search over all class partitions, not tabulated
    The new version searches all possible ways to split the five AMD stages into two groups to build ROC curves for each feature; the best partition is a data-driven modeling choice that affects every flip and every accuracy number.
  • 50% missing-value feature exclusion threshold = 50%
    Features with more than half of values missing were dropped when building the refined dataset; the authors state this threshold reflected the population threshold used earlier, making it a hand-chosen modeling decision.
assumptions (4)
  • domain assumption AMD stage labels derived from retinal image grading are treated as ground truth.
    CACTUS predicts the stage assigned by the E3 consortium grading; no validation of the labels themselves is performed, and the model is trained and evaluated against these labels.
  • domain assumption The last available visit per patient is a valid cross-sectional representation for screening classification.
    Methods and Table 1 use the last visit to obtain a populous and balanced dataset; this conditions the whole experiment on the assumption that dropout and visit timing do not introduce selection bias.
  • standard math Standard definitions of conditional probability, graph centrality, PageRank, and balanced accuracy apply as implemented.
    The method uses Equation (1) for conditional probabilities and PageRank/Degree centralities; the paper does not independently verify these assumptions on this data.
  • domain assumption Randomly-induced missingness behaves like real-world missingness.
    Resilience is measured by randomly deleting values, while the original dataset already had missing fields; informative missingness in real AMD cohorts is not modeled.

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Cite this review

Pith. "Pith review of CACTUS as a Reliable Tool for Early Classification of Age-related Macular Degeneration." pith.science (2026). https://pith.science/paper/4S3LF5FU

@misc{pith2026250614843,
  author       = {Pith},
  title        = {Pith review of: CACTUS as a Reliable Tool for Early Classification of Age-related Macular Degeneration},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4S3LF5FU}},
  note         = {Machine review of arXiv:2506.14843}
}
read the original abstract

Machine Learning (ML) is used to tackle various tasks, such as disease classification and prediction. The effectiveness of ML models relies heavily on having large amounts of complete data. However, healthcare data is often limited or incomplete, which can hinder model performance. Additionally, issues like the trustworthiness of solutions vary with the datasets used. The lack of transparency in some ML models further complicates their understanding and use. In healthcare, particularly in the case of Age-related Macular Degeneration (AMD), which affects millions of older adults, early diagnosis is crucial due to the absence of effective treatments for reversing progression. Diagnosing AMD involves assessing retinal images along with patients' symptom reports. There is a need for classification approaches that consider genetic, dietary, clinical, and demographic factors. Recently, we introduced the -Comprehensive Abstraction and Classification Tool for Uncovering Structures-(CACTUS), aimed at improving AMD stage classification. CACTUS offers explainability and flexibility, outperforming standard ML models. It enhances decision-making by identifying key factors and providing confidence in its results. The important features identified by CACTUS allow us to compare with existing medical knowledge. By eliminating less relevant or biased data, we created a clinical scenario for clinicians to offer feedback and address biases.

Figures

Figures reproduced from arXiv: 2506.14843 by the authors.

Figure 1
Figure 1. A diagram of the internal functioning of CACTUS. Each continuous feature in the dataset is partitioned into flips representing high ( U) or low ( D). Otherwise, it is kept unaltered. The threshold to partition the continuous features is found using a receiver operating characteristic (ROC) curve. Two populations are required to build the ROC curve and find the most appropriate threshold to abstract the features. The… view at source ↗
Figure 2
Figure 2. The performance achieved by CACTUS compared with ML models. CACTUS’s Degree (CDG, blue stars), PageRank (CPR, green diamonds), and Probabilistic (CPB, orange hexagons) implementations are shown along standard ML models: Linear Regression (LR, red circles), Random Forest (RF, purple plus signs), Ridge (brown dots), Stochastic Gradient Descent (SGD, pink squares), Support Vector Machine (SVM, grey triangles), and eXtr… view at source ↗
Figure 3
Figure 3. The relationship between confidence (X-axis), balanced accuracy (Y-axis, first row), and population (Y-axis, second row). Confidence is reported on the X-axis, while balanced accuracy and population are reported on the Y-axis of, respectively, the first and second rows. This metric is provided for the Degree (A, B), PageRank (C, D), and Probabilistic (E, F) classification methods in CACTUS. The confidence (blue line… view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: The ranks of the 9 most important features for the PageRank metric computed on the filtered dataset. The most relevant features are sorted from the highest (upper left, 0) to the lowest (lower right, 8), as indicated in the title of each plot between parentheses. The d…
Figure 5
Figure 5. Figure 5: Feature ranks computed by CACTUS through the [PITH_FULL_IMAGE:figures/full_fig_p027_5.png]
Figure 6
Figure 6. Figure 6: The ranks of the features computed by CACTUS through the [PITH_FULL_IMAGE:figures/full_fig_p028_6.png]
Figure 7
Figure 7. Figure 7: Feature ranks computed by CACTUS through the [PITH_FULL_IMAGE:figures/full_fig_p035_7.png]

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. CLARK: Closed-loop Learning for Adaptive Reasoning over Knowledge Graphs

    cs.AI 2026-07 conditional novelty 4.0 of 10

    CLARK improves medical classification by iteratively enriching a knowledge graph with mined logical rules calibrated through LP^MLN probabilistic reasoning.

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Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.