The global incidence of Diabetes mellitus is on the rise, leading to significant health challenges by increasing the pressure on healthcare systems. To enhance patient lower complications, early prevention is essential. This paper discusses the validation of an artificial intelligence (AI) prediction tool designed to identify individuals at a sensitive risk of diabetes. Advanced machine learning algorithms like random forest and support vector machine are used in this research, and neural networks are used to identify the patterns and relationships of diabetes onset, utilizing a comprehensive dataset that encompasses demographic, clinical, and elements from specific data sources such as electronic records and population surveys. Feature selection was implemented to improve the model's clarity and effectiveness.
Kala et al. (Wed,) studied this question.