The Development of a Machine Learning-based Diabetes Screening System

Diabetes is a chronic disease that affects millions of people worldwide. Early detection and timely intervention are crucial for preventing severe complications. Traditional diagnostic methods can be time-consuming and invasive. This research explores the application of machine learning techniques to develop a non-invasive and efficient diabetes screening system. By analyzing various patient data, including demographic, clinical, and lifestyle factors, the model aims to accurately predict the likelihood of developing diabetes. The study compares the performance of different classification algorithms, such as Gradient Boosting, Random Forest, and Logistic Regression, in terms of accuracy, precision, recall, and AUC-ROC. The results demonstrate the effectiveness of Gradient Boosting in accurately identifying individuals at risk of diabetes. This research has the potential to significantly improve early diabetes detection and contribute to better disease management and prevention.