Machine-learning algorithms in screening for type 2 diabetes mellitus: Data from Fasa Adults Cohort Study

INTRODUCTION: The application of machine learning (ML) is increasingly growing in biomedical sciences. This study aimed to evaluate factors associated with type 2 diabetes mellitus (T2DM) and compare the performance of ML methods in identifying individuals with the disease in an Iranian setting. METHODS: Using the baseline data from Fasa Adult Cohort Study (FACS) and in a sex-stratified manner, we studied factors associated with T2DM by applying seven different ML methods including Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbours (KNN), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGB) and Bagging classifier (BAG). We further compared the performance of these methods; for each algorithm, accuracy, precision, sensitivity, specificity, F1 score, and Area Under Curve (AUC) were calculated. RESULTS: 10,112 participants were recruited between 2014 and 2016, of whom 1246 had T2DM at baseline. 4566 (45%) participants were males, aged between 35 and 70 years. For males, age, sugar consumption, and history of hospitalization were the most weighted variables regarding their importance in screening for T2DM using the GBM model, respectively; these variables were sugar consumption, urine blood, and age for females. GBM outperformed other models for both males and females with AUC of 0.75 (0.69-0.82) and 0.76 (0.71-0.80), and F1 score of 0.33 (0.27-0.39) and 0.42 (0.38-0.46), respectively. GBM also showed a sensitivity of 0.24 (0.19-0.29) and a specificity of 0.98 (0.96-1.0) in males and a sensitivity of 0.38 (0.34-0.42) and specificity of 0.92 (0.89-0.95) in females. Notably, close performance characteristics were detected among other ML models. CONCLUSIONS: GBM model might achieve better performance in screening for T2DM in a south Iranian population

Association between dietary total antioxidant capacity and the risk of stroke: a nested case-control study

Abstract Background Oxidative stress after ischemic stroke contribute to neuronal cell injury. Unhealthy and unbalanced dietary patterns can increase the risk of several diseases, including stroke and cardiometabolic ones. However, the association between dietary total antioxidant capacity (DTAC) of antioxidant and stroke is controversial. Our study aimed to establish a correlation between DTAC and its impact on the occurrence of stroke. Methods This nested case–control study included 79 stroke cases and 158 healthy controls. We used data from the Fasa Adults Cohort Study (FACS) comprising 10,035 individuals at baseline. To assess the nutritional status of each individual, a 125-item food frequency questionnaire (FFQ) has been used to evaluate their dietary habits and intakes over the past year. DTAC was calculated using the ferric-reducing antioxidant power (FRAP) international databases. The stroke was confirmed by an experienced neurologist using standard imaging methods. Conditional logistic regression analyses were performed to evaluate the association between DTAC and stroke. Results The assessment of DTAC revealed that there was no statistically significant distinction between cases (mean ± SD: 5.31 ± 2.65) and controls (5.16 ± 2.80) with a p-value of 0.95. Even after adjusting for the potentially important confounding factors such as age, sex, event time, energy intake, smoking, hypertension, and diabetes, the association remains non-significant (adjusted odds ratio (OR) = 1.06, 95% CI: 0.94, 1.20, p-value = 0.33). Conclusions Our results did not confirm a significant link between DTAC and stroke risk. These findings emphasize the intricate interplay of factors influencing stroke risk and highlight the need for further research to unravel these relationships more comprehensively

Machine‐learning algorithms in screening for type 2 diabetes mellitus: Data from Fasa Adults Cohort Study

Abstract Introduction The application of machine learning (ML) is increasingly growing in biomedical sciences. This study aimed to evaluate factors associated with type 2 diabetes mellitus (T2DM) and compare the performance of ML methods in identifying individuals with the disease in an Iranian setting. Methods Using the baseline data from Fasa Adult Cohort Study (FACS) and in a sex‐stratified manner, we studied factors associated with T2DM by applying seven different ML methods including Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), K‐Nearest Neighbours (KNN), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGB) and Bagging classifier (BAG). We further compared the performance of these methods; for each algorithm, accuracy, precision, sensitivity, specificity, F1 score, and Area Under Curve (AUC) were calculated. Results 10,112 participants were recruited between 2014 and 2016, of whom 1246 had T2DM at baseline. 4566 (45%) participants were males, aged between 35 and 70 years. For males, age, sugar consumption, and history of hospitalization were the most weighted variables regarding their importance in screening for T2DM using the GBM model, respectively; these variables were sugar consumption, urine blood, and age for females. GBM outperformed other models for both males and females with AUC of 0.75 (0.69–0.82) and 0.76 (0.71–0.80), and F1 score of 0.33 (0.27–0.39) and 0.42 (0.38–0.46), respectively. GBM also showed a sensitivity of 0.24 (0.19–0.29) and a specificity of 0.98 (0.96–1.0) in males and a sensitivity of 0.38 (0.34–0.42) and specificity of 0.92 (0.89–0.95) in females. Notably, close performance characteristics were detected among other ML models. Conclusions GBM model might achieve better performance in screening for T2DM in a south Iranian population