Predicting Type 2 diabetes onset age using machine learning: A case study in KSA. - Faten Al-Hussein; Laleh Tafakori; Mali Abdollahian; Khalid Al-Shali; Ahmed Al-Hejin (PLoS ONE)
The rising prevalence of Type 2 Diabetes (T2D) in Saudi Arabia presents significant healthcare challenges. Estimating the age at onset of T2D can aid early interventions, potentially reducing complications due to late diagnoses. This study, conducted at King Abdulaziz Medical University Hospital, aims to predict the age at onset of T2D using Multiple Linear Regression (MLR), Artificial Neural Networks (ANN), Random Forest (RF), Support Vector Regression (SVR), and Decision Tree Regression (DTR). It also seeks to identify key predictors influencing the age at onset of T2D in Saudi Arabia, which ranks 7th globally in prevalence. Medical records from 1,000 diabetic patients from 2018 to 2022 that contain demographic, lifestyle, and lipid profile data are used to develop the models. The average onset age was 65 years, with the most common onset range between 40 and 90 years. The MLR and RF models provided the best fit, achieving R2 values of 0.90 and 0.89, root mean square errors (RMSE) of 0.07 and 0.01, and mean absolute errors (MAE) of 0.05 and 0.13, respectively, using the logarithmic transformation of the onset age. Key factors influencing the age at onset included triglycerides (TG), total cholesterol (TC), high-density lipoprotein (HDL), ferritin, body mass index (BMI), systolic blood pressure (SBP), white blood cell count (WBC), diet, and vitamin D levels. This study is the first in Saudi Arabia to employ MLR, ANN, RF, SVR, and DTR models to predict T2D onset age, providing valuable tools for healthcare practitioners to monitor and design intervention strategies aimed at reducing the impact of T2D in the region.