Prediction of type II diabetes using linear regression

Individual onset prediction models for diabetes are widely recognized as an effective tool for ascertaining the risky population of diabetes and for selecting intervention programs, which can provide a basis for health management, disease prevention decisions and evaluation of intervention programs. Currently, many countries have established individual prediction models for diabetes in different populations, however, due to the lack of cohort data, it is not yet possible to predict the incidence of diabetes in different populations. Meanwhile, due to the lack of data on different populations, effective topometric models for individual risk of diabetes have not yet been established. Therefore, it is necessary to establish suitable prediction models for the onset of diabetes at the individual level in adults. In this study, regression models were developed to compare different possible causes of diabetes, and according to the individual results of the chances of getting diabetes in 768 researchers, it was found that there were more cases of high BMI, diabetic pedigree function and blood glucose levels. Because this is a new disease in developing countries, there is a potential improvement for clinical prediction models to be developed. I believe that this model can be used to generate credible predictions regarding type 2 diabetes and ultimately improve on existing preventive interventions or come up with more acceptable models to lower diabetes's incidence and expense.