Few-shot Cross-country Generalization of Tabular Machine Learning and Foundation Models for Childhood Anemia Prediction under Distribution Shift

• URL: http://arxiv.org/abs/2605.26589v1
• Date: Tue, 26 May 2026 06:20:20 GMT
• Title: Few-shot Cross-country Generalization of Tabular Machine Learning and Foundation Models for Childhood Anemia Prediction under Distribution Shift
• Abstract summary: Childhood anemia affects around 40% of children aged 6-59 months globally.<n>We used DHS data from 16 countries across Africa, Asia, Latin America, the Caucasus, and the Middle East.<n>Child age, altitude, and height-for-age z-score as dominant predictors, followed by wealth and maternal education.
• Score: 1.0456326026076763
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Childhood anemia affects around 40% of children aged 6-59 months globally and arises from heterogeneous factors, limiting model generalizability. We evaluate a transformer-based tabular foundation model against classical supervised methods under cross-country and data-scarce settings. We used DHS data from 16 countries across Africa, Asia, Latin America, the Caucasus, and the Middle East (n=68,856). We compared Logistic Regression, XGBoost, LightGBM, and TabPFN v2.6. Performance was assessed using AUC-ROC, Brier score, and ECE. Generalization was evaluated using leave-one-country-out (LOCO), reverse-LOCO, and few-shot settings. Subgroup analyses included sex, age, residence, maternal education, and wealth. Feature importance was estimated using SHAP. TabPFN outperformed classical models in low-data regimes (<200 samples), showing higher discrimination and better calibration. Across countries, it achieved the lowest Brier score (0.042) and ECE (0.203). Under full-data settings, AUC-ROC ranged from 0.59-0.76 with small between-model differences ($\leq 0.05$). LOCO performance was stable (0.58-0.69), driven by country context. Reverse-LOCO showed asymmetric transferability. Subgroup performance was consistent with no systematic demographic bias. SHAP identified child age, altitude, and height-for-age z-score as dominant predictors, followed by wealth and maternal education. Performance in childhood anemia prediction is driven more by population variation than model choice. TabPFN provides advantages in low-resource settings through improved discrimination and calibration, highlighting foundation models as promising tools for data-scarce global health prediction.

Related papers

• An Integrated Framework for Explainable, Fair, and Observable Hospital Readmission Prediction: Development and Validation on MIMIC-IV [0.0]
  We constructed a cohort of 415231 adult admissions from the MIMIC-IV database.<n>This framework delivers competitive performance, clinically actionable explanations, and strong demographic equity.
  arXiv  Detail & Related papers  (2026-04-24T13:21:44Z)
• Can Persona-Prompted LLMs Emulate Subgroup Values? An Empirical Analysis of Generalisability and Fairness in Cultural Alignment [75.88231994139132]
  We show that even state-of-the-art models like GPT-4.1 achieve only 57.4% accuracy in predicting modal preferences.<n>We find significant pre-existing performance biases, where models better emulate young, male, Chinese, and Christian personas.
  arXiv  Detail & Related papers  (2026-04-14T15:06:13Z)
• Multilevel Determinants of Overweight and Obesity Among U.S. Children Aged 10-17: Comparative Evaluation of Statistical and Machine Learning Approaches Using the 2021 National Survey of Children's Health [0.2538209532048867]
  We analyze 18,792 children aged 10-17 years from the National Survey of Children's Health.<n>Overweight/obesity is defined using BMI categories. Predictors included diet, physical activity, sleep, parental stress, socioeconomic conditions, adverse experiences, and neighborhood characteristics.<n>Performance is evaluated using AUC, accuracy, precision, recall, F1 score, and Brier score.
  arXiv  Detail & Related papers  (2026-02-23T19:31:44Z)
• Child Mortality Prediction in Bangladesh: A Decade-Long Validation Study [0.0]
  The Demographic and Health Surveys (DHS) data from Bangladesh for 2011-2022, with n = 33,962, are used in this paper.<n>We trained the model on (2011-2014) data, validated it on 2017 data, and tested it on 2022 data.<n>Eight years after the initial test of the model, a genetic algorithm-based Neural Architecture Search found a single-layer neural architecture to be superior to XGBoost.
  arXiv  Detail & Related papers  (2026-02-03T19:18:50Z)
• Predicting Anemia Among Under-Five Children in Nepal Using Machine Learning and Deep Learning [1.8183349008726755]
  We analyzed Nepal Demographic and Health Survey (NDHS 2022) microdata comprising 1,855 children.<n>Child age, recent fever, household size, maternal anemia, and parasite deworming were consistently selected.<n>Machine learning and deep learning models can provide competitive anemia prediction.
  arXiv  Detail & Related papers  (2026-02-01T04:00:12Z)
• Explainable Admission-Level Predictive Modeling for Prolonged Hospital Stay in Elderly Populations: Challenges in Low- and Middle-Income Countries [65.4286079244589]
  Prolonged length of stay (pLoS) is a significant factor associated with the risk of adverse in-hospital events.<n>We develop and explain a predictive model for pLos using admission-level patient and hospital administrative data.
  arXiv  Detail & Related papers  (2026-01-07T23:35:24Z)
• Using Pre-training and Interaction Modeling for ancestry-specific disease prediction in UK Biobank [69.90493129893112]
  Recent genome-wide association studies (GWAS) have uncovered the genetic basis of complex traits, but show an under-representation of non-European descent individuals. Here, we assess whether we can improve disease prediction across diverse ancestries using multiomic data.
  arXiv  Detail & Related papers  (2024-04-26T16:39:50Z)
• Strict baselines for Covid-19 forecasting and ML perspective for USA and Russia [105.54048699217668]
  Covid-19 allows researchers to gather datasets accumulated over 2 years and to use them in predictive analysis. We present the results of a consistent comparative study of different types of methods for predicting the dynamics of the spread of Covid-19 based on regional data for two countries: the United States and Russia.
  arXiv  Detail & Related papers  (2022-07-15T18:21:36Z)
• Assessing Social Determinants-Related Performance Bias of Machine Learning Models: A case of Hyperchloremia Prediction in ICU Population [6.8473641147443995]
  We evaluated four classifiers built to predict Hyperchloremia, a condition that often results from aggressive fluids administration in the ICU population. We observed that adding social determinants features in addition to the lab-based ones improved model performance on all patients. We urge future researchers to design models that proactively adjust for potential biases and include subgroup reporting.
  arXiv  Detail & Related papers  (2021-11-18T03:58:50Z)
• Balancing Biases and Preserving Privacy on Balanced Faces in the Wild [50.915684171879036]
  There are demographic biases present in current facial recognition (FR) models. We introduce our Balanced Faces in the Wild dataset to measure these biases across different ethnic and gender subgroups. We find that relying on a single score threshold to differentiate between genuine and imposters sample pairs leads to suboptimal results. We propose a novel domain adaptation learning scheme that uses facial features extracted from state-of-the-art neural networks.
  arXiv  Detail & Related papers  (2021-03-16T15:05:49Z)
• LOGAN: Local Group Bias Detection by Clustering [86.38331353310114]
  We argue that evaluating bias at the corpus level is not enough for understanding how biases are embedded in a model. We propose LOGAN, a new bias detection technique based on clustering. Experiments on toxicity classification and object classification tasks show that LOGAN identifies bias in a local region.
  arXiv  Detail & Related papers  (2020-10-06T16:42:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.