Research on Type 2 Diabetes Risk based on Lifestyle Factors

Abstract

Diabetes is a global chronic disease, and the number of patients and medical expenses are increasing. It is predicted that by 2045, the number of adult diabetics in the world will reach 693 million, and the expenditure on health care will increase to 958 billion US dollars. Diabetes can be divided into two types: type 1 and type 2, of which type 2 diabetes accounts for 95% of all diabetes cases. In this study, principal component analysis and stochastic forest algorithm are used to evaluate the performance of the model by using large-scale survey data and ROC curve, which provides a new idea for diabetes prediction research. The results show that the stochastic forest algorithm performs well in dealing with high-dimensional data, while the principal component analysis method can reduce the dimension of high-dimensional data, reduce redundant features and improve the prediction ability of the model. Therefore, the method proposed in this paper can help medical researchers to make better use of diabetes data for research, and provide a new method for diabetes risk prediction.