Predictive Modelling of Diabetes Detection Using User Interface Designing Tool

This study delves into the application of predictive modelling in healthcare, specifically focusing on diabetes. Utilizing logistic regression, a well-established statistical algorithm, the research aims to predict the likelihood of diabetes by analysing a diverse dataset encompassing demographic details, medical history, lifestyle factors, and biochemical indicators. The model's performance is evaluated through metrics such as accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC-ROC). Key predictors associated with diabetes risk are identified, enhancing our understanding of the disease, and facilitating targeted interventions. Feature selection techniques are applied for model interpretability, and comparative assessments with alternative algorithms validate the efficacy of logistic regression. The findings contribute valuable insights to personalized healthcare strategies and public health initiatives, emphasizing the significance of early identification and intervention for improved patient outcomes.