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REVIEW 4 major objections 8 minor 35 references

A deep learning and machine learning approach to predict neonatal death in the context of S\~ao Paulo

T0 review · 4 major / 8 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read This paper claims that an LSTM neural net trained on 1.4 million São Paulo birth records predicts newborn death within 28 days with 99% accuracy, the best of seven models tested.

desk verdict The paper's headline 99% LSTM accuracy is not supported by its evaluation: balancing before splitting and majority-class accuracy on an imbalanced dataset make the central claim an artifact. read the letter →

arxiv 2506.16929 v1 pith:Z3GOEN47 submitted 2025-06-20 cs.LG cs.AI

Mohon Raihan , Plabon Kumar Saha , Rajan Das Gupta , A Z M Tahmidul Kabir , Afia Anjum Tamanna , Md. Harun-Ur-Rashid , Adnan Bin Abdus Salam , Md Tanvir Anjum
show 1 more author
A Z M Ahteshamul Kabir
This is my paper
classification cs.LGcs.AI
keywords neonatalmortalitypredictioninfantmachinelearningdeepLSTMXGBoostrandomforestSãoPaulobirthrecords
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 sets out to find the most accurate machine-learning or deep-learning model for predicting whether a newborn will die within 28 days, using 1,427,906 birth and death records from São Paulo between 2012 and 2018. Across seven algorithms, the long short-term memory network (LSTM) reaches 99% accuracy, while XGBoost and random forest each reach 94%; the authors conclude that LSTM is the model best suited to real-life use as an early-warning tool. The practical point is that a nearly error-free predictor would let clinicians direct extra care to at-risk babies and mothers. The paper also reports that the classical models were trained on a NearMiss-undersampled dataset to handle the rarity of deaths, while the deep learning models were trained on the full dataset.

What carries the argument

The load-bearing object is the long short-term memory (LSTM) network, a recurrent-neural-network variant that selectively retains patterns over long input sequences; here it is applied to 23 tabular birth-record features to classify the binary label. The auxiliary mechanism for the classical baselines is NearMiss undersampling, which balances the overwhelmingly common 'survived' class against the rare 'death' class before the 70:30 train/test split. The LSTM's 99% result carries the paper's claim that the deep model is the best choice for deployment, while the 94% results from XGBoost and random forest provide the comparison that makes the gap visible.

What would settle it

Re-run the pipeline on the original 1,427,906 records, splitting 70:30 before any balancing, and compute LSTM accuracy, precision, recall, and ROC-AUC on the natural-prevalence test set; if accuracy falls substantially below 99% or recall on the rare death class drops sharply, the paper's real-world performance claim is falsified.

Watch

Extended reading notes

Core claim

On the authors' own terms, the central discovery is that an LSTM classifier trained on the full São Paulo dataset predicts the 'Death within 28 Days' label with 99% accuracy, 99% precision, 99% recall, and a 0.99 F1-score. This is reported as beating a CNN (98%) and the best classical models, XGBoost and random forest (94% each). The paper interprets these numbers to mean that when a newborn's recorded parameters are passed in, the model returns the correct outcome 99 times out of 100, making LSTM the recommended choice for deciding whether precautionary care is needed. The conclusion extends this to say that such intelligent models could help doctors and health workers be aware and prepared ahead of time.

Load-bearing premise

The reported accuracy assumes that the balanced test set created by undersampling, with roughly equal numbers of survivors and deaths, represents the real São Paulo newborn population well enough for 99% accuracy to describe real-world use.

Editorial extensions

If this is right

  • A hospital or public-health agency in São Paulo could use the LSTM as a screening step: a newborn's record would be flagged as at-risk, prompting earlier care.
  • The five-percentage-point gap over XGBoost and random forest suggests the deep model learns patterns the tree ensembles miss, and if reproduced it justifies the added complexity of training an LSTM.
  • Because the dataset spans 1.4 million births over seven years, the model's training signal is large enough to support subgroup analysis by birth weight, gestational age, or maternal characteristics.
  • The reported 99% recall implies very few missed deaths in the balanced test setting, which matters for a screening tool where a false negative is the costliest error.

Reading between the lines

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

  • My inference: the reported 99% accuracy almost certainly overstates real-world performance, because the test set was built after NearMiss undersampling and therefore contains far more deaths than the actual São Paulo newborn population; a natural-prevalence test set would be the real check.
  • My inference: because the input features are tabular rather than explicitly sequential, the LSTM's apparent edge may come from model capacity or regularization rather than from its sequence memory; comparing it to a carefully tuned feed-forward network or a tabular transformer would separate those explanations.
  • My inference: the same pipeline could be turned into a risk score computed at birth registration, and the approach could be applied to other Brazilian state birth cohorts to test whether the 99% result transfers.
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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 / 8 minor

Summary. The paper applies six machine learning and deep learning models—logistic regression, KNN, support vector classifier, XGBoost, random forest, CNN, and LSTM—to 1,427,906 birth records from São Paulo to predict whether a newborn dies within 28 days. It reports that XGBoost and random forest achieve 94% accuracy and that LSTM achieves 99% accuracy, concluding that LSTM is the most suitable model for real-life neonatal mortality prediction. The central evaluation, however, is invalid for the stated real-world claim: for the classical models, NearMiss undersampling is applied before the 70:30 train/test split, so the test set is artificially balanced rather than representative of natural prevalence; for the deep learning models, the reported metrics on a highly imbalanced population are not interpretable without confusion matrices and natural-prevalence evaluation. The manuscript also lacks code, a correct data citation, hyperparameters, architecture details, confidence intervals, and any form of external validation.

Significance. If the 99% accuracy claim were properly validated, the work would have practical significance for early-warning systems in neonatal care, and the use of a 1.4-million-record population dataset is a clear strength. The paper also compares several standard algorithms, which is a useful framing. However, the reported results are not established as genuine predictive skill: the evaluation protocol cannot support the headline accuracy, and the manuscript does not provide the reproducibility artifacts (code, data, detailed experimental configuration) needed to verify or repair the claim. The potential significance is therefore not realized in the present manuscript.

major comments (4)
  1. [§2.3 and §2.4] NearMiss undersampling is applied to the entire dataset in Section 2.3, before the 70:30 train/test split described in Section 2.4. This means the test set used for logistic regression, KNN, SVC, XGBoost, and random forest has artificially equal class proportions rather than the natural prevalence of neonatal death. Accuracy, precision, recall, and F1 reported in Table 1 for these models are therefore not estimates of real-world performance on actual newborns. The split must be performed before any balancing, with NearMiss applied only to the training portion and the test set left untouched.
  2. [§2.6 and Table 1] For the deep learning models, Section 2.6 states that NearMiss was 'evaded' and the models were trained on the complete dataset. In a population where neonatal deaths are rare, an always-survive classifier already achieves approximately 99% accuracy, so the LSTM's reported 99% accuracy is not, by itself, evidence of predictive skill. The manuscript says confusion matrices were generated but does not show them, and no PR-AUC, recall at natural prevalence, or calibration metrics are reported. The claim that LSTM is 'most suitable' for real-life use requires evaluation on a test set with natural class prevalence and requires reporting true-positive and false-positive rates, not only rounded aggregate scores.
  3. [§3 and Table 1] No confidence intervals, cross-validation results, or statistical significance tests are reported for any model, and no hyperparameters, network architectures, optimizer settings, or training epochs are provided. Without these details, the observed differences between models—for example, 94% for XGBoost/random forest versus 99% for LSTM—cannot be interpreted as meaningful or reproducible. The conclusion that LSTM is the best model for deployment is therefore unsupported by the evidence presented.
  4. [§2.1 and reference [4]] The dataset is described as 'secondary information on Children's births and deaths in the city of São Paulo' but is cited to reference [4], which is a WHO fact sheet on newborn mortality, not a dataset source. The actual provenance, version, and access information for the São Paulo data are missing. This is a load-bearing reproducibility issue because the entire study depends on this dataset.
minor comments (8)
  1. [§2.6] Subsections 3.6.1 through 3.6.7 appear under Section 2.6 but are numbered as if they belong to Section 3; they should be renumbered as 2.6.1 through 2.6.7.
  2. [Abstract] The phrase '26.693 babies out of 1,000 births' should read '26.693 per 1,000 births', and 'Macro Trades' should be 'Macrotrends'.
  3. [§2.1] The text says 'All the column descriptions are given in Table 1', but Table 1 contains model comparison results, not column descriptions; the column-description table appears to be missing or mislabeled.
  4. [§3] The recall formula is written as TP/(TP + F); the denominator should be TP + FN. Similar notation ambiguities appear in the true-positive and false-positive rate definitions.
  5. [Figure 1] The flowchart shows NearMiss applied before the split for all models, but Section 2.6 says NearMiss was not used for deep learning; the flowchart should distinguish the two pipelines.
  6. [Figure 9] The accuracy comparison graph does not include error bars or any measure of variance, so the visual comparison is not informative.
  7. [§2.4] The text says the split 'randomly chooses the columns for test and training'; it should say rows or samples.
  8. [Throughout] There are numerous typographical and grammatical issues, including 'logical regression', 'nearest neighborhood', 'Near Miss' versus 'nearmiss' inconsistency, and 'using LSTM of the model shall be best suited'; a thorough language edit is needed.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: the reported accuracies are empirical measurements, not results that reduce to their inputs by construction.

full rationale

The paper's derivation chain is purely empirical: it collects 1.4 million birth records, preprocesses them, applies NearMiss undersampling to the classical-ML stream, splits into 70:30 train/test, trains seven classifiers, and reports Table 1 accuracies. No parameter is fitted to a subset and then reported as a prediction of that same subset, no quantity is defined in terms of the target it is claimed to predict, and no load-bearing conclusion rests on a self-citation or an imported uniqueness theorem. The strongest concern in the manuscript is the ordering in Sections 2.3 and 2.4: NearMiss is applied before the train/test split, so the classical-model test set is artificially balanced, and the deep-learning stream in Section 2.6 is evaluated on the original imbalanced data where a majority-class predictor would already score near 99%. These are serious threats to the external validity of the claimed 99% accuracy, but they are evaluation-design flaws, not circularity: the Table 1 numbers are measured on held-out data rather than being equal to the training inputs or to a fitted parameter by construction. Since no step in the paper's own equations or citations makes the outcome equivalent to its input, the circularity score is 0.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The central claim rests almost entirely on the quality of an external administrative dataset and on evaluation choices that are not independently verified. The only explicit free choices are the unspecified model configurations and the NearMiss balancing procedure. No new theoretical entities are introduced.

free parameters (2)
  • Model hyperparameters and architectures (RF, XGBoost, CNN, LSTM) = not reported
    The paper does not state tree counts, depths, learning rates, layer sizes, epochs, or activation functions. These choices directly affect the reported accuracies and are not reproducible (Sections 2.5-2.6).
  • NearMiss undersampling configuration = not reported
    The balancing ratio and NearMiss version are not specified. The procedure is applied before the split, changing the test distribution (Section 2.3).
assumptions (4)
  • domain assumption The São Paulo birth-death records are complete and the 'Death within 28 Days' labels are correct.
    The entire training and evaluation rest on the accuracy of the administrative dataset described in Section 2.1. No data quality audit or label validation is provided.
  • domain assumption Features used for prediction are available at the time a prediction would be made in practice.
    The paper does not discuss whether all 23 features are known at birth. If some are recorded only later, the model would be unusable in the claimed real-life setting (Sections 2.1 and 4).
  • domain assumption Accuracy is an appropriate evaluation metric for a rare-event classification problem.
    With rare neonatal deaths, accuracy can be high even for a trivial majority-class model. The paper reports only accuracy, precision, recall, and F1, without AUROC or calibration (Section 3, Table 1).
  • standard math Scikit-learn, TensorFlow, and Keras implementations are correct and suitable for the task.
    The study relies on these libraries in Sections 2.5 and 2.6 without verification. This is a background tooling assumption, not a weakness specific to the authors.

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Cite this review

Pith. "Pith review of A deep learning and machine learning approach to predict neonatal death in the context of S\~ao Paulo." pith.science (2026). https://pith.science/paper/Z3GOEN47

@misc{pith2026250616929,
  author       = {Pith},
  title        = {Pith review of: A deep learning and machine learning approach to predict neonatal death in the context of S\~ao Paulo},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/Z3GOEN47}},
  note         = {Machine review of arXiv:2506.16929}
}
read the original abstract

Neonatal death is still a concerning reality for underdeveloped and even some developed countries. Worldwide data indicate that 26.693 babies out of 1,000 births die, according to Macro Trades. To reduce this number, early prediction of endangered babies is crucial. Such prediction enables the opportunity to take ample care of the child and mother so that early child death can be avoided. In this context, machine learning was used to determine whether a newborn baby is at risk. To train the predictive model, historical data of 1.4 million newborns was used. Machine learning and deep learning techniques such as logical regression, K-nearest neighbor, random forest classifier, extreme gradient boosting (XGBoost), convolutional neural network, and long short-term memory (LSTM) were implemented using the dataset to identify the most accurate model for predicting neonatal mortality. Among the machine learning algorithms, XGBoost and random forest classifier achieved the best accuracy with 94%, while among the deep learning models, LSTM delivered the highest accuracy with 99%. Therefore, using LSTM appears to be the most suitable approach to predict whether precautionary measures for a child are necessary.

Figures

Figures reproduced from arXiv: 2506.16929 by the authors.

Figure 1
Figure 1. Flowchart of the methodology 2.1. Data collection This dataset is based on secondary information on Children's births and deaths in the city of São Paulo between 2012 and 2018 [4]. There are 24 columns and 1,427,906 rows in the dataset. All the column descriptions are given in [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Value count of categorical features from the data set 2.2. Data pre-processing From the data set, the null value and missing data were identified. This would lead to an anomaly in the trained models. To resolve the issue, the rows were deleted if a single missing value was found to be null. The missing values were eliminated using NumPy's dropNa method. 2.3. Near miss method The analysis begins with the identificati… view at source ↗
Figure 3
Figure 3. Correlation between each dataset column with heatmap [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: Count of label values before applying nearmiss [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 5
Figure 5. Figure 5: Analysis between alive and death in weight [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]
Figure 6
Figure 6. Figure 6: Count of label values after applying nearmiss [PITH_FULL_IMAGE:figures/full_fig_p006_6.png]
Figure 7
Figure 7. Figure 7: Convolutional neural network [25] [PITH_FULL_IMAGE:figures/full_fig_p007_7.png]
Figure 8
Figure 8. Figure 8: Structure of a convolutional long short-term memory [26] 3. RESULTS AND DISCUSSION All the recent previous studies relating to or closely similar to our titular topic were reviewed above. Most of these studies had either of the following few limitations such as data li…
Figure 9
Figure 9. Figure 9: Accuracy comparison between different models 4. CONCLUSION Data-driven decision-making is used in almost every aspect of modern times. This detailed study looks to analyze data based on a very critical issue for developing and underdeveloped countries. To create a robu…

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Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.