Affiliation:
1. Student Research Committee, School of Medicine Fasa University of Medical Sciences Fasa Iran
2. USERN Office Fasa University of Medical Sciences Fasa Iran
3. Noncommunicable Diseases Research Center Fasa University of Medical Science Fasa Iran
4. Student Research Committee, School of Medicine Shiraz University of Medical Sciences Shiraz Iran
5. Health Policy Research Center, School of Medicine Shiraz University of Medical Sciences Shiraz Iran
6. Clinical Research Development Unit, Valiasr Hospital Fasa University of Medical Sciences Fasa Iran
7. Data Science and Biostatistics Department Julius Global Health Utrecht The Netherlands
Abstract
AbstractIntroductionThe 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.MethodsUsing 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.Results10,112 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.ConclusionsGBM model might achieve better performance in screening for T2DM in a south Iranian population.