The Impact of Managerial Approach to Untreated Type -2 Diabetes using AI Techniques

Abstract

Individuals aged 20 years and over are considered. We identified participants as having diabetes if they had a HbA1c level greater than 6.5 %. People with diabetes who say they do not actually obtain care is deemed to be untreated for the purposes of this research. In this research, we used logistic regression to assess which risk factors were correlated with untreated diabetes. The aim of Review Machine learning (ML) is to diagnose, cure, and prevent diabetes. While a number of ML models have been created, they are not relevant to real- world scenarios yet. There has been a significant disconnect between ML architects, health care researchers, physicians, and patients in their technologies. Our aim is to perform an in-depth analysis on ML to recognize the potential and shortcomings of the technology. Recent advances in the development of insulin delivery devices, diabetes retinopathy diagnostic methods, and other medical studies have significantly helped people diagnosed with diabetes. Compared with these, the usage of statistical methods for diabetes treatment is only at an early level. The Food and Drug Administration (FDA) employs several highly creative ideas to get their drugs to the consumer. Description ML offers a fantastic chance to handle diabetes with improved strategies and technology.