Journal of health, population, and nutrition · 2025
Nutritional intake of micronutrient and macronutrient and type 2 diabetes: machine learning schemes.
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Rashidmayvan Mohammad, Mansoori Amin, Derakhshan-Nezhad Elahe, Tanbakuchi Davoud, Sangin Fatemeh, Mohammadi-Bajgiran Maryam, Abedsaeidi Malihehsadat, Ghazizadeh Sara, Sarabi MohammadReza Mohammad Taghizadeh, Rezaee Ali, Ferns Gordon, Esmaily Habibollah, Ghayour-Mobarhan Majid
Аннотация
BACKGROUND: Diabetes mellitus, an endocrine system disease, is a common disease involving many patients worldwide. Many studies are performed to evaluate the correlation between micronutrients/macronutrients on diabetes but few of them have a high statistical population and a long follow-up period. We aimed to investigate the relationship between intake of macro/micronutrients and the incidence of type 2 diabetes (T2D) using logistic regression (LR) and a decision tree (DT) algorithm for machine learning.
METHOD: Our research explores supervised machine learning models to identify T2D patients using the Mashhad Cohort Study dataset. The study population comprised 9704 individuals aged 35-65 years were enrolled regarding their T2D status, and those with T2D history. 15% of individuals are diabetic and 85% of them are non-diabetic. For ten years (until 2020), the participants in the study were monitored to determine the incidence of T2D. LR is a statistical model applied in dichotomous response variable modeling. All data were analyzed by SPSS (Version 22) and SAS JMP software.
RESULT: Nutritional intake in the T2D group showed that potassium, calcium, magnesium, zinc, iodine, carotene, vitamin D, tryptophan, and vitamin B12 had an inverse correlation with the incidence of diabetes (p < 0.05). While phosphate, iron, and chloride had a positive relationship with the risk of T2D (p < 0.05). Also, the T2D group significantly had higher carbohydrate and protein intake (p-value < 0.05).
CONCLUSION: Machine learning models can identify T2D risk using questionnaires and blood samples. These have implications for electronic health records that can be explored further.
METHOD: Our research explores supervised machine learning models to identify T2D patients using the Mashhad Cohort Study dataset. The study population comprised 9704 individuals aged 35-65 years were enrolled regarding their T2D status, and those with T2D history. 15% of individuals are diabetic and 85% of them are non-diabetic. For ten years (until 2020), the participants in the study were monitored to determine the incidence of T2D. LR is a statistical model applied in dichotomous response variable modeling. All data were analyzed by SPSS (Version 22) and SAS JMP software.
RESULT: Nutritional intake in the T2D group showed that potassium, calcium, magnesium, zinc, iodine, carotene, vitamin D, tryptophan, and vitamin B12 had an inverse correlation with the incidence of diabetes (p < 0.05). While phosphate, iron, and chloride had a positive relationship with the risk of T2D (p < 0.05). Also, the T2D group significantly had higher carbohydrate and protein intake (p-value < 0.05).
CONCLUSION: Machine learning models can identify T2D risk using questionnaires and blood samples. These have implications for electronic health records that can be explored further.