# Prediction of Diabetes Using Statistical and Machine Learning Modelling Techniques
## 5 Results  
This section presents the findings from the regression models used to predict diabetes prevalence based on demographic and behavioural risk factors. The models were assessed using four key statistical evaluation metrics: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R\({}^{2}\)). These metrics allowed for an objective comparison of the models' performance and prediction accuracy. Table 2 presents the regression modelling results for diabetes prevalence among men and women aged \(\geq\)25 years during the training period (1999-2013). The results show a steady increase in diabetes prevalence over time, with a higher prevalence among men than women. In men, diabetes prevalence increased from 9.7% in 1999 to 13.9% in 2013, reflecting an absolute increase of 4.2 percentage points (pp) and an annual increase of 0.3 pp. Similarly, the prevalence in women rose from 7% in 1999 to 11% in 2013, at the same  
Figure 3: ANN architecture. annual increase of 0.3 pp. The performance evaluation metrics for the training data, shown in Table 3, revealed that ANFIS achieved the best results, with RMSE = 0.04 and R\({}^{2}\) = 0.99 for men and RMSE = 0.02 and R\({}^{2}\) = 0.99 for women, indicating that ANFIS was highly accurate in modelling the observed trends, outperforming other regression techniques. Using the test dataset (2014-2025), projections were made assuming the observed 1999-2013 trends continue. Table 4 presents these estimates, where the projected diabetes prevalence for men is expected to rise from 14.2% in 2014 to 17.6% in 2025, and for women it is projected to increase from 12.4% in 2014 to 17.3% in 2025. The low MSE and RMSE values across models further confirm the reliability of these projections, indicating that the models are able to make accurate predictions. Figure 4 illustrates the total estimated diabetes prevalence from 1999 to 2025 for both men and women, showing an upward trajectory in all cases. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Men**} & \multicolumn{4}{c}{**Women**} \\ \hline
**Year** & **MLR** & **ANFIS** & **ANN** & **SVR** & **BLM** & **MLR** & **ANFIS** & **ANN** & **SVR** & **BLM** \\ \hline
1999 & 9.3 & 9.70 & 9.70 & 9.88 & 9.70 & 6.8 & 7.00 & 7.00 & 6.70 & 6.92 \\ \hline
2000 & 9.6 & 9.80 & 9.70 & 10.03 & 9.81 & 7.0 & 7.12 &  
The prevalence of diabetes was also analysed across six ten-year age groups. The findings indicate that there was a lower prevalence in younger age groups, which steadily increased with age. The highest prevalence was observed among individuals aged 55-74 years. Figures 5 and 6 visualise these trends across age groups for men and women, respectively, further confirming the strong correlation between age and diabetes prevalence. Figure 7 highlights the projected trends for behavioural risk factors associated with diabetes. Smoking prevalence is expected to increase from 11% in 1999 to 16.05% in 2025, while obesity rates will rise sharply from 16.7% to 51.7% over the same period. In contrast, physical inactivity is predicted to drop significantly from 96% in 1999 to 61.1% in 2025, although this percentage remains dangerously high. Furthermore, gender-based disparities in risk factor prevalence were observed. For instance, men had consistently higher smoking rates than women (21.1% vs. 0.9% in 1999; 28.4% vs. 3.7% in 2025), while women exhibited higher obesity prevalence (20.3% vs. 13.1% in 1999; 58.4% vs. 45% in 2025).