A Fuzzy Approach to T2Dm in Young Adults in India

Abstract

Designing a Fuzzy Expert System (FES) for the diagnosis of Type 2 Diabetes Mellitus in young adults is the goal of this work. Diabetes is a chronic condition brought on by a lack of either insulin production or action, or both, leading to long-term consequences. Diabetes prevalence has been quickly increasing over the world. Numerous techniques have been developed to detect diabetes at an early stage; however, the majority of these techniques lack interpretability, making it impossible to describe the diagnostic procedure. Although there is currently no cure for diabetes, early diagnosis allows patients to begin treatment sooner and lowers the risk of serious complications.

There are number of techniques used for diagnosis of diabetes, few methodologies or techniques used in Mathematical Science including Neural Network, Naive Bayes, and Support Vector Machine, are used in the previous system to diagnose diabetes. But the current system's performance is ineffective. Existing methodologies do not diagnose diabetes in its early stages. In this essay, we suggest a more effective and efficient method for diagnosing diabetes. Early stage fuzzy inference system diabetes diagnostic determines a person's diabetes status based on the information provided and treatment recommendations for a specific kind of diabetes