# Prediction of Diabetes Using Statistical and Machine Learning Modelling Techniques
## 6 Discussion  
This study evaluated and compared multiple regression-based machine learning models for predicting diabetes prevalence based on demographic and behavioural risk factors. The results highlight the strengths of various models and provide valuable insights into the future trajectory of diabetes prevalence. The performance of each regression model was summarised in Table 3, using evaluation metrics such as MSE, RMSE, MAPE, and R\({}^{2}\). Overall, the ANFIS model demonstrated superior predictive accuracy, achieving the lowest RMSE and the highest R\({}^{2}\) values for both the men's and women's training datasets. Specifically, ANFIS achieved RMSE = 0.04 for men and 0.02 for women, and R\({}^{2}\) = 0.99 for both groups, showcasing its remarkable ability to model diabetes trends with precision. As anticipated, SVR, with a linear kernel, yielded results similar to those of MLR, while BLR and ANN also displayed reasonably good performance. However, the ANFIS model consistently outperformed all other models, confirming our hypothesis that a hybrid approach would offer better predictive accuracy.  
These findings indicate that ANFIS is the most effective model for predicting diabetes prevalence. Its ability to capture complex, nonlinear relationships within the dataset makes it particularly valuable for healthcare decision making, especially in predicting long-term trends. While the models provided good performance, certain models were better suited for specific datasets, underlining the importance of selecting the most appropriate models for different demographic groups.  
The increasing prevalence of diabetes across all age groups is a concerning trend. As highlighted in the results, the highest rates of diabetes were found among individuals aged 55-74 years, with both men and women showing steady increases in prevalence over time. The findings also highlight gender-based disparities in the prevalence of diabetes, with men generally exhibiting higher rates of the disease but women often experiencing more severe health consequences. This underscores the importance of addressing gender-specific health strategies in diabetes prevention and management.  
In addition to diabetes prevalence, this study also investigated the trends in behavioural risk factors, including smoking, obesity, and physical inactivity. The results show that while smoking and obesity are expected to increase over time, there is a promising decrease in physical inactivity. However, despite this improvement, the overall prevalence of inactivity remains uncomfortably high, indicating that public health initiatives must focus on increasing physical activity among the population. The gender differences in behavioural risk factors are also significant, with men exhibiting higher smoking rates  
Figure 11: Performance metrics of regression models, women’s data.  
and women showing higher obesity rates. These findings suggest the need for targeted interventions based on gender-specific patterns.  
This study demonstrates significant strengths, such as the use of advanced predictive models and a well-structured dataset. However, there are certain limitations, including the reliance on self-reported data for behavioural variables, which may introduce bias. Future research could address these limitations by incorporating a more diverse dataset and additional predictors.