Food insecurity and its related indicators in an Iranian population: An application of machine learning methods in data from Golestan Province
5 affiliations
- 1Department of Biostatistics, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
- 2Proteomics Research Center, Department of Biostatistics, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
- 3Health Management and Social Development Research Centre, Department of Biostatistics and Epidemiology, Golestan University of Medical Sciences, Gorgan, Islamic Republic of Iran.
- 4Health Management and Social Development Research Center, Department of Nutrition, Golestan University of Medical Sciences, Gorgan, Islamic Republic of Iran.
- 5Department of Biostatistics, School of Public Health, Iran University of Medical Sciences, Tehran, Iran.
Abstract
Background: Household food insecurity (FI) is a major public health concern associated with socioeconomic vulnerability. Unfortunately, the prevalence of food insecurity in Iran has increased substantially in recent years due to the economic challenges associated with prolonged international sanctions. In this context, this study aimed to estimate the prevalence of household FI in northern Iran and compare the predictive performance of traditional and machine learning algorithms for classifying households according to presence of FI. Methods: This cross-sectional study included 4,948 households from Golestan Province in north of Iran. The study sample was selected using the multistage cluster sampling between December 2021 and September 2022. FI was assessed using the Household Food Insecurity Access Scale. The data was analyzed using the logistic regression, random forest, support vector machine, decision tree, naïve Bayes, and artificial neural network models. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, balanced accuracy, Brier score, calibration plots, and DeLong’s test for pairwise AUC comparisons. The bootstrap resampling was applied to assess the stability and uncertainty of predictor importance. In addition, the estimated rankings derived from the logistic regression and random forest models were compared using the Spearman’s correlation coefficient. Results: The prevalence of household FI was 61.7%, including 32.3% mild, 20.3% moderate, and 9.1% severe FI. The multivariable logistic regression analysis showed that lower income, lower educational attainment, unemployment, lack of insurance coverage, absence of supplementary insurance, lack of car or house ownership, fewer employed household members, larger household size, being under coverage of supportive organizations, having incurable disease, and Turkmen ethnicity remained associated with FI. The pairwise DeLong’s test showed modest differences between the fitted models, with no statistically significant difference between the logistic regression and either neural network or SVM radial models after Bonferroni correction. Household income was ranked as the most important predictor in both the logistic regression and random forest models. Conclusions: In general, our estimates revealed that household FI was highly prevalent in the sample under study and strongly associated with socioeconomic disadvantage. Logistic regression, neural network, and SVM models demonstrated relatively similar predictive performance, while more complex ML approaches did not show substantial practical improvement over conventional methods in this dataset. Given the serious consequences of household food insecurity, health policymakers should pay more attention to improve socioeconomic conditions of households residing in the studied areas.
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BibTeX
@article{nassajtorshizi2026food,
title = {{Food insecurity and its related indicators in an Iranian population: An application of machine learning methods in data from Golestan Province}},
author = {Fatemeh Nassaj-Torshizi and Farid Zayeri and Nasser Behnampour and Mohammad-Reza Honarvar and Morteza Mohammadzadeh},
journal = {PLOS One},
year = {2026},
month = oct,
doi = {10.1371/journal.pone.0361131},
url = {https://doi.org/10.1371/journal.pone.0361131},
note = {CC BY},
}
RIS
TY - JOUR
TI - Food insecurity and its related indicators in an Iranian population: An application of machine learning methods in data from Golestan Province
AU - Fatemeh Nassaj-Torshizi
AU - Farid Zayeri
AU - Nasser Behnampour
AU - Mohammad-Reza Honarvar
AU - Morteza Mohammadzadeh
PY - 2026
DA - 2026/10/30
JO - PLOS One
DO - 10.1371/journal.pone.0361131
UR - https://doi.org/10.1371/journal.pone.0361131
AB - Background: Household food insecurity (FI) is a major public health concern associated with socioeconomic vulnerability. Unfortunately, the prevalence of food insecurity in Iran has increased substantially in recent years due to the economic challenges associated with prolonged international sanctions. In this context, this study aimed to estimate the prevalence of household FI in northern Iran and compare the predictive performance of traditional and machine learning algorithms for classifying households according to presence of FI. Methods: This cross-sectional study included 4,948 households from Golestan Province in north of Iran. The study sample was selected using the multistage cluster sampling between December 2021 and September 2022. FI was assessed using the Household Food Insecurity Access Scale. The data was analyzed using the logistic regression, random forest, support vector machine, decision tree, naïve Bayes, and artificial neural network models. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, balanced accuracy, Brier score, calibration plots, and DeLong’s test for pairwise AUC comparisons. The bootstrap resampling was applied to assess the stability and uncertainty of predictor importance. In addition, the estimated rankings derived from the logistic regression and random forest models were compared using the Spearman’s correlation coefficient. Results: The prevalence of household FI was 61.7%, including 32.3% mild, 20.3% moderate, and 9.1% severe FI. The multivariable logistic regression analysis showed that lower income, lower educational attainment, unemployment, lack of insurance coverage, absence of supplementary insurance, lack of car or house ownership, fewer employed household members, larger household size, being under coverage of supportive organizations, having incurable disease, and Turkmen ethnicity remained associated with FI. The pairwise DeLong’s test showed modest differences between the fitted models, with no statistically significant difference between the logistic regression and either neural network or SVM radial models after Bonferroni correction. Household income was ranked as the most important predictor in both the logistic regression and random forest models. Conclusions: In general, our estimates revealed that household FI was highly prevalent in the sample under study and strongly associated with socioeconomic disadvantage. Logistic regression, neural network, and SVM models demonstrated relatively similar predictive performance, while more complex ML approaches did not show substantial practical improvement over conventional methods in this dataset. Given the serious consequences of household food insecurity, health policymakers should pay more attention to improve socioeconomic conditions of households residing in the studied areas.
SN - 1932-6203
ER -