Diabetes Risk Analysis based on Machine Learning LASSO Regression Model - Wang, Sihao; Chen, Yizhi; Cui, Zhengrong; Lin, Luqi; Zong, Yanqi (Journal of Theory and Practice of Engineering Science, 2024)
With the continuous application of artificial intelligence in the field of medical research, machine learning has been widely used to solve many complex problems in the medical field. However, there are many risk factors affecting the development of diabetes, which are far more complex than the traditional disease prediction model. LASSO (Least Absolute Shrinkage and Selection Operator) regression model is an intelligent machine learning algorithm, which has the advantages of strong anti-overfitting ability and is not susceptible to collinearity between variables. The prediction model based on LASSO regression algorithm is conducive to finding and identifying different models and nonlinear relationships among multi-dimensional factors, so as to accurately predict the incidence of diabetes. In disease detection, the role of LASSO regression models is to help identify key characteristic variables associated with disease, thereby improving the predictive accuracy and interpretability of the models. By reducing uncorrelated variables, LASSO is able to process high-dimensional data more efficiently, reducing the risk of overfitting, and improving the model's ability to generalize. Based on these advantages of LASSO algorithm, this paper analyzes the risk detection of diabetes on the basis of machine learning.