Predicting diabetes using Cohen's Kappa blending ensemble learning

Diabetes is a well-known risk factor for early mortality and disability. As signatories to the 2030 Agenda for Sustainable Development, Member States set an ambitious objective of a one-third reduction in early death due to non-communicable diseases (NCDs), which includes diabetes. Nonetheless, the current economic impact of diabetes on countries, individuals, and healthcare requires an agent means of its early detection. However, early detection of diabetes with conventional techniques is a considerable challenge for the healthcare industry and physicians. This study proposed a blended ensemble predictive model with Cohen's Kappa correlation-based base-learners selection to decrease unnecessary diabetes-related mortality through early detection. The empirical outcome shows that our proposed predictive model outperformed existing state-of-the-art approaches for predicting diabetes, thus resulting in enhanced diabetes prediction ability.